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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.13797">arXiv:2502.13797</a> <span> [<a href="https://arxiv.org/pdf/2502.13797">pdf</a>, <a href="https://arxiv.org/format/2502.13797">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Caruso%2C+A">Alessandro Caruso</a>, <a href="/search/physics?searchtype=author&query=Venturin%2C+J">Jacopo Venturin</a>, <a href="/search/physics?searchtype=author&query=Giambagli%2C+L">Lorenzo Giambagli</a>, <a href="/search/physics?searchtype=author&query=Rolando%2C+E">Edoardo Rolando</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.13797v2-abstract-short" style="display: inline;"> Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inherently local and can suffer from information flow bottlenecks. This is particularly problematic when modeling large molecular systems, where dispersion forces and local electric field variations drive collective structural… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13797v2-abstract-full').style.display = 'inline'; document.getElementById('2502.13797v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.13797v2-abstract-full" style="display: none;"> Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inherently local and can suffer from information flow bottlenecks. This is particularly problematic when modeling large molecular systems, where dispersion forces and local electric field variations drive collective structural changes. Existing solutions face challenges related to computational cost and scalability. We introduce RANGE, a model-agnostic framework that employs an attention-based aggregation-broadcast mechanism that significantly reduces oversquashing effects, and achieves remarkable accuracy in capturing long-range interactions at a negligible computational cost. Notably, RANGE is the first virtual-node message-passing implementation to integrate attention with positional encodings and regularization to dynamically expand virtual representations. This work lays the foundation for next-generation of machine-learned force fields, offering accurate and efficient modeling of long-range interactions for simulating large molecular systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.13797v2-abstract-full').style.display = 'none'; document.getElementById('2502.13797v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.09988">arXiv:2501.09988</a> <span> [<a href="https://arxiv.org/pdf/2501.09988">pdf</a>, <a href="https://arxiv.org/format/2501.09988">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Fluorescence emission of the JUNO liquid scintillator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Beretta%2C+M">M. Beretta</a>, <a href="/search/physics?searchtype=author&query=Houria%2C+F">F. Houria</a>, <a href="/search/physics?searchtype=author&query=Ferraro%2C+F">F. Ferraro</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">D. Basilico</a>, <a href="/search/physics?searchtype=author&query=Brigatti%2C+A">A. Brigatti</a>, <a href="/search/physics?searchtype=author&query=Caccianiga%2C+B">B. Caccianiga</a>, <a href="/search/physics?searchtype=author&query=Caslini%2C+A">A. Caslini</a>, <a href="/search/physics?searchtype=author&query=Landini%2C+C">C. Landini</a>, <a href="/search/physics?searchtype=author&query=Lombardi%2C+P">P. Lombardi</a>, <a href="/search/physics?searchtype=author&query=Pelicci%2C+L">L. Pelicci</a>, <a href="/search/physics?searchtype=author&query=Percalli%2C+E">E. Percalli</a>, <a href="/search/physics?searchtype=author&query=Ranucci%2C+G">G. Ranucci</a>, <a href="/search/physics?searchtype=author&query=Re%2C+A+C">A. C. Re</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">C. Clementi</a>, <a href="/search/physics?searchtype=author&query=Ortica%2C+F">F. Ortica</a>, <a href="/search/physics?searchtype=author&query=Romani%2C+A">A. Romani</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">V. Antonelli</a>, <a href="/search/physics?searchtype=author&query=Giammarchi%2C+M+G">M. G. Giammarchi</a>, <a href="/search/physics?searchtype=author&query=Miramonti%2C+L">L. Miramonti</a>, <a href="/search/physics?searchtype=author&query=Saggese%2C+P">P. Saggese</a>, <a href="/search/physics?searchtype=author&query=Torri%2C+M+D+C">M. D. C. Torri</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">S. Aiello</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">G. Andronico</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">A. Barresi</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">A. Bergnoli</a> , et al. (43 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.09988v2-abstract-short" style="display: inline;"> JUNO is a huge neutrino detector that will use 20 kton of organic liquid scintillator as its detection medium. The scintillator is a mixture of linear alkyl benzene (LAB), 2.5 g/L of 2,5-diphenyloxazole (PPO) and 3 mg/L of 1,4-Bis(2-methylstyryl)benzene (Bis-MSB). The main goal of JUNO is to determine the Neutrino Mass Ordering [1, 2, 3]. In order to achieve this purpose, good energy and position… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09988v2-abstract-full').style.display = 'inline'; document.getElementById('2501.09988v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.09988v2-abstract-full" style="display: none;"> JUNO is a huge neutrino detector that will use 20 kton of organic liquid scintillator as its detection medium. The scintillator is a mixture of linear alkyl benzene (LAB), 2.5 g/L of 2,5-diphenyloxazole (PPO) and 3 mg/L of 1,4-Bis(2-methylstyryl)benzene (Bis-MSB). The main goal of JUNO is to determine the Neutrino Mass Ordering [1, 2, 3]. In order to achieve this purpose, good energy and position reconstruction is required, hence a complete understanding of the optical characteristics of the liquid scintillator is mandatory. In this paper we present the measurements on the JUNO scintillator emission spectrum, absorption length and fluorescence time distribution performed respectively with a spectrofluorimeter, a spectrophotometer and a custom made setup <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09988v2-abstract-full').style.display = 'none'; document.getElementById('2501.09988v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.04526">arXiv:2407.04526</a> <span> [<a href="https://arxiv.org/pdf/2407.04526">pdf</a>, <a href="https://arxiv.org/format/2407.04526">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Bonneau%2C+K">Klara Bonneau</a>, <a href="/search/physics?searchtype=author&query=Lederer%2C+J">Jonas Lederer</a>, <a href="/search/physics?searchtype=author&query=Templeton%2C+C">Clark Templeton</a>, <a href="/search/physics?searchtype=author&query=Rosenberger%2C+D">David Rosenberger</a>, <a href="/search/physics?searchtype=author&query=M%C3%BCller%2C+K">Klaus-Robert M眉ller</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.04526v1-abstract-short" style="display: inline;"> Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution. One of the main criticisms to machine learned potentials is that the energy inferred by the network is not… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04526v1-abstract-full').style.display = 'inline'; document.getElementById('2407.04526v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.04526v1-abstract-full" style="display: none;"> Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution. One of the main criticisms to machine learned potentials is that the energy inferred by the network is not as interpretable as in more traditional approaches where a simpler functional form is used. Here we address this problem by extending tools recently proposed in the nascent field of Explainable Artificial Intelligence (XAI) to coarse-grained potentials based on graph neural networks (GNN). We demonstrate the approach on three different coarse-grained systems including two fluids (methane and water) and the protein NTL9. On these examples, we show that the neural network potentials can be in practice decomposed in relevance contributions to different orders, that can be directly interpreted and provide physical insights on the systems of interest. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04526v1-abstract-full').style.display = 'none'; document.getElementById('2407.04526v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.03448">arXiv:2407.03448</a> <span> [<a href="https://arxiv.org/pdf/2407.03448">pdf</a>, <a href="https://arxiv.org/format/2407.03448">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Accurate nuclear quantum statistics on machine-learned classical effective potentials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Zaporozhets%2C+I">Iryna Zaporozhets</a>, <a href="/search/physics?searchtype=author&query=Musil%2C+F">F茅lix Musil</a>, <a href="/search/physics?searchtype=author&query=Kapil%2C+V">Venkat Kapil</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.03448v1-abstract-short" style="display: inline;"> The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03448v1-abstract-full').style.display = 'inline'; document.getElementById('2407.03448v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.03448v1-abstract-full" style="display: none;"> The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD). Specifically, we employ a machine-learned potential to accurately represent corrections to classical potentials, thereby significantly reducing the computational cost of simulating NQEs. We validate our approach using four distinct systems: Morse potential, Zundel cation, single water molecule, and bulk water. Our framework allows us to accurately compute position-dependent static properties, as demonstrated by the excellent agreement obtained between the machine-learned potential and computationally intensive PIMD calculations, even in the presence of strong NQEs. This approach opens the way to the development of transferable machine-learned potentials capable of accurately reproducing NQEs in a wide range of molecular systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03448v1-abstract-full').style.display = 'none'; document.getElementById('2407.03448v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.01286">arXiv:2407.01286</a> <span> [<a href="https://arxiv.org/pdf/2407.01286">pdf</a>, <a href="https://arxiv.org/format/2407.01286">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Learning data efficient coarse-grained molecular dynamics from forces and noise </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Durumeric%2C+A+E+P">Aleksander E. P. Durumeric</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Y">Yaoyi Chen</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.01286v1-abstract-short" style="display: inline;"> Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for modeling biomolecules. However, MLCG models currently require large amounts of data from reference atomistic molecular dynamics or substantial computation for training. Denoising score matching -- the technology behind the widely popular diffusion models -- has simultaneously emerged as a machine-learning framework… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01286v1-abstract-full').style.display = 'inline'; document.getElementById('2407.01286v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.01286v1-abstract-full" style="display: none;"> Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for modeling biomolecules. However, MLCG models currently require large amounts of data from reference atomistic molecular dynamics or substantial computation for training. Denoising score matching -- the technology behind the widely popular diffusion models -- has simultaneously emerged as a machine-learning framework for creating samples from noise. Models in the first category are often trained using atomistic forces, while those in the second category extract the data distribution by reverting noise-based corruption. We unify these approaches to improve the training of MLCG force-fields, reducing data requirements by a factor of 100 while maintaining advantages typical to force-based parameterization. The methods are demonstrated on proteins Trp-Cage and NTL9 and published as open-source code. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01286v1-abstract-full').style.display = 'none'; document.getElementById('2407.01286v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.12901">arXiv:2406.12901</a> <span> [<a href="https://arxiv.org/pdf/2406.12901">pdf</a>, <a href="https://arxiv.org/format/2406.12901">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.physletb.2024.139141">10.1016/j.physletb.2024.139141 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Gavrikov%2C+A">A. Gavrikov</a>, <a href="/search/physics?searchtype=author&query=Cerrone%2C+V">V. Cerrone</a>, <a href="/search/physics?searchtype=author&query=Serafini%2C+A">A. Serafini</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">R. Brugnera</a>, <a href="/search/physics?searchtype=author&query=Garfagnini%2C+A">A. Garfagnini</a>, <a href="/search/physics?searchtype=author&query=Grassi%2C+M">M. Grassi</a>, <a href="/search/physics?searchtype=author&query=Jelmini%2C+B">B. Jelmini</a>, <a href="/search/physics?searchtype=author&query=Lastrucci%2C+L">L. Lastrucci</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">S. Aiello</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">G. Andronico</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">V. Antonelli</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">A. Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">D. Basilico</a>, <a href="/search/physics?searchtype=author&query=Beretta%2C+M">M. Beretta</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">A. Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Borghesi%2C+M">M. Borghesi</a>, <a href="/search/physics?searchtype=author&query=Brigatti%2C+A">A. Brigatti</a>, <a href="/search/physics?searchtype=author&query=Bruno%2C+R">R. Bruno</a>, <a href="/search/physics?searchtype=author&query=Budano%2C+A">A. Budano</a>, <a href="/search/physics?searchtype=author&query=Caccianiga%2C+B">B. Caccianiga</a>, <a href="/search/physics?searchtype=author&query=Cammi%2C+A">A. Cammi</a>, <a href="/search/physics?searchtype=author&query=Caruso%2C+R">R. Caruso</a>, <a href="/search/physics?searchtype=author&query=Chiesa%2C+D">D. Chiesa</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">C. Clementi</a>, <a href="/search/physics?searchtype=author&query=Dusini%2C+S">S. Dusini</a> , et al. (43 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.12901v2-abstract-short" style="display: inline;"> Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrou… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12901v2-abstract-full').style.display = 'inline'; document.getElementById('2406.12901v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.12901v2-abstract-full" style="display: none;"> Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12901v2-abstract-full').style.display = 'none'; document.getElementById('2406.12901v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This is a post-peer-review, pre-copyedit version of an article published in Phys. Lett. B. The final published version is available online: https://www.sciencedirect.com/science/article/pii/S0370269324006993</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Physics Letters B 860, 139141 (2025) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.01381">arXiv:2406.01381</a> <span> [<a href="https://arxiv.org/pdf/2406.01381">pdf</a>, <a href="https://arxiv.org/format/2406.01381">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Distillation and Stripping purification plants for JUNO liquid scintillator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Landini%2C+C">C. Landini</a>, <a href="/search/physics?searchtype=author&query=Beretta%2C+M">M. Beretta</a>, <a href="/search/physics?searchtype=author&query=Lombardi%2C+P">P. Lombardi</a>, <a href="/search/physics?searchtype=author&query=Brigatti%2C+A">A. Brigatti</a>, <a href="/search/physics?searchtype=author&query=Montuschi%2C+M">M. Montuschi</a>, <a href="/search/physics?searchtype=author&query=Parmeggiano%2C+S">S. Parmeggiano</a>, <a href="/search/physics?searchtype=author&query=Ranucci%2C+G">G. Ranucci</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">V. Antonelli</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">D. Basilico</a>, <a href="/search/physics?searchtype=author&query=Caccianiga%2C+B">B. Caccianiga</a>, <a href="/search/physics?searchtype=author&query=Giammarchi%2C+M+G">M. G. Giammarchi</a>, <a href="/search/physics?searchtype=author&query=Miramonti%2C+L">L. Miramonti</a>, <a href="/search/physics?searchtype=author&query=Percalli%2C+E">E. Percalli</a>, <a href="/search/physics?searchtype=author&query=Re%2C+A+C">A. C. Re</a>, <a href="/search/physics?searchtype=author&query=Saggese%2C+P">P. Saggese</a>, <a href="/search/physics?searchtype=author&query=Torri%2C+M+D+C">M. D. C. Torri</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">S. Aiello</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">G. Andronico</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">A. Barresi</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">A. Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Borghesi%2C+M">M. Borghesi</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">R. Brugnera</a>, <a href="/search/physics?searchtype=author&query=Bruno%2C+R">R. Bruno</a>, <a href="/search/physics?searchtype=author&query=Budano%2C+A">A. Budano</a>, <a href="/search/physics?searchtype=author&query=Cammi%2C+A">A. Cammi</a> , et al. (42 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.01381v1-abstract-short" style="display: inline;"> The optical and radiochemical purification of the scintillating liquid, which will fill the central detector of the JUNO experiment, plays a crucial role in achieving its scientific goals. Given its gigantic mass and dimensions and an unprecedented target value of about 3% @ 1 MeV in energy resolution, JUNO has set severe requirements on the parameters of its scintillator, such as attenuation leng… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01381v1-abstract-full').style.display = 'inline'; document.getElementById('2406.01381v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.01381v1-abstract-full" style="display: none;"> The optical and radiochemical purification of the scintillating liquid, which will fill the central detector of the JUNO experiment, plays a crucial role in achieving its scientific goals. Given its gigantic mass and dimensions and an unprecedented target value of about 3% @ 1 MeV in energy resolution, JUNO has set severe requirements on the parameters of its scintillator, such as attenuation length (Lat>20 m at 430 nm), transparency, light yield, and content of radioactive contaminants (238U,232Th<10-15 g/g). To accomplish these needs, the scintillator will be processed using several purification methods, including distillation in partial vacuum and gas stripping, which are performed in two large scale plants installed at the JUNO site. In this paper, layout, operating principles, and technical aspects which have driven the design and construction of the distil- lation and gas stripping plants are reviewed. The distillation is effective in enhancing the optical properties and removing heavy radio-impurities (238U,232Th, 40K), while the stripping process exploits pure water steam and high-purity nitrogen to extract gaseous contaminants (222Rn, 39Ar, 85Kr, O2) from the scintillator. The plant operating parameters have been tuned during the recent com- missioning phase at the JUNO site and several QA/QC measurements and tests have been performed to evaluate the performances of the plants. Some preliminary results on the efficiency of these purification processes will be shown. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01381v1-abstract-full').style.display = 'none'; document.getElementById('2406.01381v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.19879">arXiv:2405.19879</a> <span> [<a href="https://arxiv.org/pdf/2405.19879">pdf</a>, <a href="https://arxiv.org/format/2405.19879">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2024.169730">10.1016/j.nima.2024.169730 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Refractive index in the JUNO liquid scintillator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Zhang%2C+H+S">H. S. Zhang</a>, <a href="/search/physics?searchtype=author&query=Beretta%2C+M">M. Beretta</a>, <a href="/search/physics?searchtype=author&query=Cialdi%2C+S">S. Cialdi</a>, <a href="/search/physics?searchtype=author&query=Yang%2C+C+X">C. X. Yang</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+J+H">J. H. Huang</a>, <a href="/search/physics?searchtype=author&query=Ferraro%2C+F">F. Ferraro</a>, <a href="/search/physics?searchtype=author&query=Cao%2C+G+F">G. F. Cao</a>, <a href="/search/physics?searchtype=author&query=Reina%2C+G">G. Reina</a>, <a href="/search/physics?searchtype=author&query=Deng%2C+Z+Y">Z. Y. Deng</a>, <a href="/search/physics?searchtype=author&query=Suerra%2C+E">E. Suerra</a>, <a href="/search/physics?searchtype=author&query=Altilia%2C+S">S. Altilia</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">V. Antonelli</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">D. Basilico</a>, <a href="/search/physics?searchtype=author&query=Brigatti%2C+A">A. Brigatti</a>, <a href="/search/physics?searchtype=author&query=Caccianiga%2C+B">B. Caccianiga</a>, <a href="/search/physics?searchtype=author&query=Giammarchi%2C+M+G">M. G. Giammarchi</a>, <a href="/search/physics?searchtype=author&query=Landini%2C+C">C. Landini</a>, <a href="/search/physics?searchtype=author&query=Lombardi%2C+P">P. Lombardi</a>, <a href="/search/physics?searchtype=author&query=Miramonti%2C+L">L. Miramonti</a>, <a href="/search/physics?searchtype=author&query=Percalli%2C+E">E. Percalli</a>, <a href="/search/physics?searchtype=author&query=Ranucci%2C+G">G. Ranucci</a>, <a href="/search/physics?searchtype=author&query=Re%2C+A+C">A. C. Re</a>, <a href="/search/physics?searchtype=author&query=Saggese%2C+P">P. Saggese</a>, <a href="/search/physics?searchtype=author&query=Torri%2C+M+D+C">M. D. C. Torri</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">S. Aiello</a> , et al. (51 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.19879v1-abstract-short" style="display: inline;"> In the field of rare event physics, it is common to have huge masses of organic liquid scintillator as detection medium. In particular, they are widely used to study neutrino properties or astrophysical neutrinos. Thanks to its safety properties (such as low toxicity and high flash point) and easy scalability, linear alkyl benzene is the most common solvent used to produce liquid scintillators for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19879v1-abstract-full').style.display = 'inline'; document.getElementById('2405.19879v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.19879v1-abstract-full" style="display: none;"> In the field of rare event physics, it is common to have huge masses of organic liquid scintillator as detection medium. In particular, they are widely used to study neutrino properties or astrophysical neutrinos. Thanks to its safety properties (such as low toxicity and high flash point) and easy scalability, linear alkyl benzene is the most common solvent used to produce liquid scintillators for large mass experiments. The knowledge of the refractive index is a pivotal point to understand the detector response, as this quantity (and its wavelength dependence) affects the Cherenkov radiation and photon propagation in the medium. In this paper, we report the measurement of the refractive index of the JUNO liquid scintillator between 260-1064 nm performed with two different methods (an ellipsometer and a refractometer), with a sub percent level precision. In addition, we used an interferometer to measure the group velocity in the JUNO liquid scintillator and verify the expected value derived from the refractive index measurements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19879v1-abstract-full').style.display = 'none'; document.getElementById('2405.19879v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.17860">arXiv:2405.17860</a> <span> [<a href="https://arxiv.org/pdf/2405.17860">pdf</a>, <a href="https://arxiv.org/format/2405.17860">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/1674-1137/ad83aa">10.1088/1674-1137/ad83aa <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Prediction of Energy Resolution in the JUNO Experiment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+Collaboration"> JUNO Collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Adamowicz%2C+K">Kai Adamowicz</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Bai%2C+W">Weidong Bai</a>, <a href="/search/physics?searchtype=author&query=Balashov%2C+N">Nikita Balashov</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">Davide Basilico</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Beretta%2C+M">Marco Beretta</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Bick%2C+D">Daniel Bick</a> , et al. (629 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.17860v2-abstract-short" style="display: inline;"> This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3\% at 1~MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.17860v2-abstract-full').style.display = 'inline'; document.getElementById('2405.17860v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.17860v2-abstract-full" style="display: none;"> This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3\% at 1~MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of the liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The results of study reveal an energy resolution of 2.95\% at 1~MeV. Furthermore, this study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data collection. Moreover, it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.17860v2-abstract-full').style.display = 'none'; document.getElementById('2405.17860v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Chinese Phys. C 49 013003 (2025) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.11599">arXiv:2405.11599</a> <span> [<a href="https://arxiv.org/pdf/2405.11599">pdf</a>, <a href="https://arxiv.org/format/2405.11599">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Lattice matched heterogeneous nucleation eliminate defective buried interface in halide perovskites </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Ahlawat%2C+P">Paramvir Ahlawat</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=Musil%2C+F">Felix Musil</a>, <a href="/search/physics?searchtype=author&query=Filip%2C+M">Maria-Andreea Filip</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.11599v1-abstract-short" style="display: inline;"> Metal halide perovskite-based semi-conducting hetero-structures have emerged as promising electronics for solar cells, light-emitting diodes, detectors, and photo-catalysts. Perovskites' efficiency, electronic properties and their long-term stability directly depend on their morphology [1-24]. Therefore, to manufacture stable and higher efficiency perovskite solar cells and electronics, it is now… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11599v1-abstract-full').style.display = 'inline'; document.getElementById('2405.11599v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.11599v1-abstract-full" style="display: none;"> Metal halide perovskite-based semi-conducting hetero-structures have emerged as promising electronics for solar cells, light-emitting diodes, detectors, and photo-catalysts. Perovskites' efficiency, electronic properties and their long-term stability directly depend on their morphology [1-24]. Therefore, to manufacture stable and higher efficiency perovskite solar cells and electronics, it is now crucial to understand their micro-structure evolution. In this study, we perform molecular dynamics simulations to investigate the formation of cesium lead bromide perovskite on interfaces. Our simulations reveal that perovskite crystallizes in a heteroepitaxial manner on widely employed oxide interfaces. This could introduce the formation of dislocations, voids and defects in the buried interface, and grain boundaries in the bulk crystal. From simulations, we find that lattice-matched interfaces could enable epitaxial ordered growth of perovskites and may prevent defect formation in the buried interface. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11599v1-abstract-full').style.display = 'none'; document.getElementById('2405.11599v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.12540">arXiv:2311.12540</a> <span> [<a href="https://arxiv.org/pdf/2311.12540">pdf</a>, <a href="https://arxiv.org/format/2311.12540">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1140/epjp/s13360-024-05704-z">10.1140/epjp/s13360-024-05704-z <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Analysis of reactor burnup simulation uncertainties for antineutrino spectrum prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Barresi%2C+A">A. Barresi</a>, <a href="/search/physics?searchtype=author&query=Borghesi%2C+M">M. Borghesi</a>, <a href="/search/physics?searchtype=author&query=Cammi%2C+A">A. Cammi</a>, <a href="/search/physics?searchtype=author&query=Chiesa%2C+D">D. Chiesa</a>, <a href="/search/physics?searchtype=author&query=Loi%2C+L">L. Loi</a>, <a href="/search/physics?searchtype=author&query=Nastasi%2C+M">M. Nastasi</a>, <a href="/search/physics?searchtype=author&query=Previtali%2C+E">E. Previtali</a>, <a href="/search/physics?searchtype=author&query=Sisti%2C+M">M. Sisti</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">S. Aiello</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">G. Andronico</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">V. Antonelli</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">D. Basilico</a>, <a href="/search/physics?searchtype=author&query=Beretta%2C+M">M. Beretta</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">A. Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Brigatti%2C+A">A. Brigatti</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">R. Brugnera</a>, <a href="/search/physics?searchtype=author&query=Bruno%2C+R">R. Bruno</a>, <a href="/search/physics?searchtype=author&query=Budano%2C+A">A. Budano</a>, <a href="/search/physics?searchtype=author&query=Caccianiga%2C+B">B. Caccianiga</a>, <a href="/search/physics?searchtype=author&query=Cerrone%2C+V">V. Cerrone</a>, <a href="/search/physics?searchtype=author&query=Caruso%2C+R">R. Caruso</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">C. Clementi</a>, <a href="/search/physics?searchtype=author&query=Dusini%2C+S">S. Dusini</a>, <a href="/search/physics?searchtype=author&query=Fabbri%2C+A">A. Fabbri</a>, <a href="/search/physics?searchtype=author&query=Felici%2C+G">G. Felici</a> , et al. (42 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.12540v3-abstract-short" style="display: inline;"> Nuclear reactors are a source of electron antineutrinos due to the presence of unstable fission products that undergo $尾^-$ decay. They will be exploited by the JUNO experiment to determine the neutrino mass ordering and to get very precise measurements of the neutrino oscillation parameters. This requires the reactor antineutrino spectrum to be characterized as precisely as possible both through… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.12540v3-abstract-full').style.display = 'inline'; document.getElementById('2311.12540v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.12540v3-abstract-full" style="display: none;"> Nuclear reactors are a source of electron antineutrinos due to the presence of unstable fission products that undergo $尾^-$ decay. They will be exploited by the JUNO experiment to determine the neutrino mass ordering and to get very precise measurements of the neutrino oscillation parameters. This requires the reactor antineutrino spectrum to be characterized as precisely as possible both through high resolution measurements, as foreseen by the TAO experiment, and detailed simulation models. In this paper we present a benchmark analysis utilizing Serpent Monte Carlo simulations in comparison with real pressurized water reactor spent fuel data. Our objective is to study the accuracy of fission fraction predictions as a function of different reactor simulation approximations. Then, utilizing the BetaShape software, we construct fissile antineutrino spectra using the summation method, thereby assessing the influence of simulation uncertainties on reactor antineutrino spectrum. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.12540v3-abstract-full').style.display = 'none'; document.getElementById('2311.12540v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Eur. Phys. J. Plus 139, 952 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.18278">arXiv:2310.18278</a> <span> [<a href="https://arxiv.org/pdf/2310.18278">pdf</a>, <a href="https://arxiv.org/format/2310.18278">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Navigating protein landscapes with a machine-learned transferable coarse-grained model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Charron%2C+N+E">Nicholas E. Charron</a>, <a href="/search/physics?searchtype=author&query=Musil%2C+F">Felix Musil</a>, <a href="/search/physics?searchtype=author&query=Guljas%2C+A">Andrea Guljas</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Y">Yaoyi Chen</a>, <a href="/search/physics?searchtype=author&query=Bonneau%2C+K">Klara Bonneau</a>, <a href="/search/physics?searchtype=author&query=Pasos-Trejo%2C+A+S">Aldo S. Pasos-Trejo</a>, <a href="/search/physics?searchtype=author&query=Venturin%2C+J">Jacopo Venturin</a>, <a href="/search/physics?searchtype=author&query=Gusew%2C+D">Daria Gusew</a>, <a href="/search/physics?searchtype=author&query=Zaporozhets%2C+I">Iryna Zaporozhets</a>, <a href="/search/physics?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/physics?searchtype=author&query=Templeton%2C+C">Clark Templeton</a>, <a href="/search/physics?searchtype=author&query=Kelkar%2C+A">Atharva Kelkar</a>, <a href="/search/physics?searchtype=author&query=Durumeric%2C+A+E+P">Aleksander E. P. Durumeric</a>, <a href="/search/physics?searchtype=author&query=Olsson%2C+S">Simon Olsson</a>, <a href="/search/physics?searchtype=author&query=P%C3%A9rez%2C+A">Adri脿 P茅rez</a>, <a href="/search/physics?searchtype=author&query=Majewski%2C+M">Maciej Majewski</a>, <a href="/search/physics?searchtype=author&query=Husic%2C+B+E">Brooke E. Husic</a>, <a href="/search/physics?searchtype=author&query=Patel%2C+A">Ankit Patel</a>, <a href="/search/physics?searchtype=author&query=De+Fabritiis%2C+G">Gianni De Fabritiis</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.18278v1-abstract-short" style="display: inline;"> The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.18278v1-abstract-full').style.display = 'inline'; document.getElementById('2310.18278v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.18278v1-abstract-full" style="display: none;"> The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.18278v1-abstract-full').style.display = 'none'; document.getElementById('2310.18278v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.05172">arXiv:2303.05172</a> <span> [<a href="https://arxiv.org/pdf/2303.05172">pdf</a>, <a href="https://arxiv.org/format/2303.05172">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2023.168680">10.1016/j.nima.2023.168680 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The JUNO experiment Top Tracker </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+Collaboration"> JUNO Collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=Aleem%2C+A">Abid Aleem</a>, <a href="/search/physics?searchtype=author&query=Alexandros%2C+T">Tsagkarakis Alexandros</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Bai%2C+W">Weidong Bai</a>, <a href="/search/physics?searchtype=author&query=Balashov%2C+N">Nikita Balashov</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">Davide Basilico</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a> , et al. (592 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.05172v1-abstract-short" style="display: inline;"> The main task of the Top Tracker detector of the neutrino reactor experiment Jiangmen Underground Neutrino Observatory (JUNO) is to reconstruct and extrapolate atmospheric muon tracks down to the central detector. This muon tracker will help to evaluate the contribution of the cosmogenic background to the signal. The Top Tracker is located above JUNO's water Cherenkov Detector and Central Detector… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05172v1-abstract-full').style.display = 'inline'; document.getElementById('2303.05172v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.05172v1-abstract-full" style="display: none;"> The main task of the Top Tracker detector of the neutrino reactor experiment Jiangmen Underground Neutrino Observatory (JUNO) is to reconstruct and extrapolate atmospheric muon tracks down to the central detector. This muon tracker will help to evaluate the contribution of the cosmogenic background to the signal. The Top Tracker is located above JUNO's water Cherenkov Detector and Central Detector, covering about 60% of the surface above them. The JUNO Top Tracker is constituted by the decommissioned OPERA experiment Target Tracker modules. The technology used consists in walls of two planes of plastic scintillator strips, one per transverse direction. Wavelength shifting fibres collect the light signal emitted by the scintillator strips and guide it to both ends where it is read by multianode photomultiplier tubes. Compared to the OPERA Target Tracker, the JUNO Top Tracker uses new electronics able to cope with the high rate produced by the high rock radioactivity compared to the one in Gran Sasso underground laboratory. This paper will present the new electronics and mechanical structure developed for the Top Tracker of JUNO along with its expected performance based on the current detector simulation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05172v1-abstract-full').style.display = 'none'; document.getElementById('2303.05172v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nucl.Instrum.Meth.A 1057 (2023) 168680 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.03910">arXiv:2303.03910</a> <span> [<a href="https://arxiv.org/pdf/2303.03910">pdf</a>, <a href="https://arxiv.org/format/2303.03910">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> JUNO sensitivity to $^7$Be, $pep$, and CNO solar neutrinos </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=Aleem%2C+A">Abid Aleem</a>, <a href="/search/physics?searchtype=author&query=Alexandros%2C+T">Tsagkarakis Alexandros</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Bai%2C+W">Weidong Bai</a>, <a href="/search/physics?searchtype=author&query=Balashov%2C+N">Nikita Balashov</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">Davide Basilico</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Beretta%2C+M">Marco Beretta</a> , et al. (592 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.03910v1-abstract-short" style="display: inline;"> The Jiangmen Underground Neutrino Observatory (JUNO), the first multi-kton liquid scintillator detector, which is under construction in China, will have a unique potential to perform a real-time measurement of solar neutrinos well below the few MeV threshold typical for Water Cherenkov detectors. JUNO's large target mass and excellent energy resolution are prerequisites for reaching unprecedented… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.03910v1-abstract-full').style.display = 'inline'; document.getElementById('2303.03910v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.03910v1-abstract-full" style="display: none;"> The Jiangmen Underground Neutrino Observatory (JUNO), the first multi-kton liquid scintillator detector, which is under construction in China, will have a unique potential to perform a real-time measurement of solar neutrinos well below the few MeV threshold typical for Water Cherenkov detectors. JUNO's large target mass and excellent energy resolution are prerequisites for reaching unprecedented levels of precision. In this paper, we provide estimation of the JUNO sensitivity to 7Be, pep, and CNO solar neutrinos that can be obtained via a spectral analysis above the 0.45 MeV threshold. This study is performed assuming different scenarios of the liquid scintillator radiopurity, ranging from the most opti mistic one corresponding to the radiopurity levels obtained by the Borexino experiment, up to the minimum requirements needed to perform the neutrino mass ordering determination with reactor antineutrinos - the main goal of JUNO. Our study shows that in most scenarios, JUNO will be able to improve the current best measurements on 7Be, pep, and CNO solar neutrino fluxes. We also perform a study on the JUNO capability to detect periodical time variations in the solar neutrino flux, such as the day-night modulation induced by neutrino flavor regeneration in Earth, and the modulations induced by temperature changes driven by helioseismic waves. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.03910v1-abstract-full').style.display = 'none'; document.getElementById('2303.03910v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.10133">arXiv:2302.10133</a> <span> [<a href="https://arxiv.org/pdf/2302.10133">pdf</a>, <a href="https://arxiv.org/format/2302.10133">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2023.168339">10.1016/j.nima.2023.168339 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Triozzi%2C+R">Riccardo Triozzi</a>, <a href="/search/physics?searchtype=author&query=Serafini%2C+A">Andrea Serafini</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Bolognesi%2C+M">Matteo Bolognesi</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">Riccardo Brugnera</a>, <a href="/search/physics?searchtype=author&query=Cerrone%2C+V">Vanessa Cerrone</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+C">Chao Chen</a>, <a href="/search/physics?searchtype=author&query=Clerbaux%2C+B">Barbara Clerbaux</a>, <a href="/search/physics?searchtype=author&query=Coppi%2C+A">Alberto Coppi</a>, <a href="/search/physics?searchtype=author&query=Corti%2C+D">Daniele Corti</a>, <a href="/search/physics?searchtype=author&query=Corso%2C+F+d">Flavio dal Corso</a>, <a href="/search/physics?searchtype=author&query=Dong%2C+J">Jianmeng Dong</a>, <a href="/search/physics?searchtype=author&query=Dou%2C+W">Wei Dou</a>, <a href="/search/physics?searchtype=author&query=Fan%2C+L">Lei Fan</a>, <a href="/search/physics?searchtype=author&query=Garfagnini%2C+A">Alberto Garfagnini</a>, <a href="/search/physics?searchtype=author&query=Gavrikov%2C+A">Arsenii Gavrikov</a>, <a href="/search/physics?searchtype=author&query=Gong%2C+G">Guanghua Gong</a>, <a href="/search/physics?searchtype=author&query=Grassi%2C+M">Marco Grassi</a>, <a href="/search/physics?searchtype=author&query=Guizzetti%2C+R+M">Rosa Maria Guizzetti</a>, <a href="/search/physics?searchtype=author&query=Hang%2C+S">Shuang Hang</a>, <a href="/search/physics?searchtype=author&query=He%2C+C">Cong He</a>, <a href="/search/physics?searchtype=author&query=Hu%2C+J">Jun Hu</a>, <a href="/search/physics?searchtype=author&query=Isocrate%2C+R">Roberto Isocrate</a>, <a href="/search/physics?searchtype=author&query=Jelmini%2C+B">Beatrice Jelmini</a> , et al. (107 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.10133v1-abstract-short" style="display: inline;"> The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the exp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.10133v1-abstract-full').style.display = 'inline'; document.getElementById('2302.10133v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.10133v1-abstract-full" style="display: none;"> The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.10133v1-abstract-full').style.display = 'none'; document.getElementById('2302.10133v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.07071">arXiv:2302.07071</a> <span> [<a href="https://arxiv.org/pdf/2302.07071">pdf</a>, <a href="https://arxiv.org/format/2302.07071">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/physics?searchtype=author&query=Durumeric%2C+A+P">Aleksander P. Durumeric</a>, <a href="/search/physics?searchtype=author&query=Charron%2C+N+E">Nicholas E. Charron</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Y">Yaoyi Chen</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.07071v1-abstract-short" style="display: inline;"> Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.07071v1-abstract-full').style.display = 'inline'; document.getElementById('2302.07071v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.07071v1-abstract-full" style="display: none;"> Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force-fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins Chignolin and Tryptophan Cage and published as open-source code. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.07071v1-abstract-full').style.display = 'none'; document.getElementById('2302.07071v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">44 pages, 19 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.04379">arXiv:2301.04379</a> <span> [<a href="https://arxiv.org/pdf/2301.04379">pdf</a>, <a href="https://arxiv.org/format/2301.04379">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2023.168255">10.1016/j.nima.2023.168255 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Mass testing of the JUNO experiment 20-inch PMTs readout electronics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Coppi%2C+A">Alberto Coppi</a>, <a href="/search/physics?searchtype=author&query=Jelmini%2C+B">Beatrice Jelmini</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Bolognesi%2C+M">Matteo Bolognesi</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">Riccardo Brugnera</a>, <a href="/search/physics?searchtype=author&query=Cerrone%2C+V">Vanessa Cerrone</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+C">Chao Chen</a>, <a href="/search/physics?searchtype=author&query=Clerbaux%2C+B">Barbara Clerbaux</a>, <a href="/search/physics?searchtype=author&query=Corti%2C+D">Daniele Corti</a>, <a href="/search/physics?searchtype=author&query=Corso%2C+F+d">Flavio dal Corso</a>, <a href="/search/physics?searchtype=author&query=Dong%2C+J">Jianmeng Dong</a>, <a href="/search/physics?searchtype=author&query=Dou%2C+W">Wei Dou</a>, <a href="/search/physics?searchtype=author&query=Fan%2C+L">Lei Fan</a>, <a href="/search/physics?searchtype=author&query=Garfagnini%2C+A">Alberto Garfagnini</a>, <a href="/search/physics?searchtype=author&query=Gavrikov%2C+A">Arsenii Gavrikov</a>, <a href="/search/physics?searchtype=author&query=Gong%2C+G">Guanghua Gong</a>, <a href="/search/physics?searchtype=author&query=Grassi%2C+M">Marco Grassi</a>, <a href="/search/physics?searchtype=author&query=Guizzetti%2C+R+M">Rosa Maria Guizzetti</a>, <a href="/search/physics?searchtype=author&query=Hang%2C+S">Shuang Hang</a>, <a href="/search/physics?searchtype=author&query=He%2C+C">Cong He</a>, <a href="/search/physics?searchtype=author&query=Hu%2C+J">Jun Hu</a>, <a href="/search/physics?searchtype=author&query=Isocrate%2C+R">Roberto Isocrate</a>, <a href="/search/physics?searchtype=author&query=Ji%2C+X">Xiaolu Ji</a>, <a href="/search/physics?searchtype=author&query=Jiang%2C+X">Xiaoshan Jiang</a> , et al. (107 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.04379v1-abstract-short" style="display: inline;"> The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04379v1-abstract-full').style.display = 'inline'; document.getElementById('2301.04379v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.04379v1-abstract-full" style="display: none;"> The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04379v1-abstract-full').style.display = 'none'; document.getElementById('2301.04379v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.08454">arXiv:2212.08454</a> <span> [<a href="https://arxiv.org/pdf/2212.08454">pdf</a>, <a href="https://arxiv.org/format/2212.08454">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2023.168322">10.1016/j.nima.2023.168322 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Validation and integration tests of the JUNO 20-inch PMTs readout electronics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Cerrone%2C+V">Vanessa Cerrone</a>, <a href="/search/physics?searchtype=author&query=von+Sturm%2C+K">Katharina von Sturm</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Bolognesi%2C+M">Matteo Bolognesi</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">Riccardo Brugnera</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+C">Chao Chen</a>, <a href="/search/physics?searchtype=author&query=Clerbaux%2C+B">Barbara Clerbaux</a>, <a href="/search/physics?searchtype=author&query=Coppi%2C+A">Alberto Coppi</a>, <a href="/search/physics?searchtype=author&query=Corso%2C+F+d">Flavio dal Corso</a>, <a href="/search/physics?searchtype=author&query=Corti%2C+D">Daniele Corti</a>, <a href="/search/physics?searchtype=author&query=Dong%2C+J">Jianmeng Dong</a>, <a href="/search/physics?searchtype=author&query=Dou%2C+W">Wei Dou</a>, <a href="/search/physics?searchtype=author&query=Fan%2C+L">Lei Fan</a>, <a href="/search/physics?searchtype=author&query=Garfagnini%2C+A">Alberto Garfagnini</a>, <a href="/search/physics?searchtype=author&query=Gong%2C+G">Guanghua Gong</a>, <a href="/search/physics?searchtype=author&query=Grassi%2C+M">Marco Grassi</a>, <a href="/search/physics?searchtype=author&query=Hang%2C+S">Shuang Hang</a>, <a href="/search/physics?searchtype=author&query=Guizzetti%2C+R+M">Rosa Maria Guizzetti</a>, <a href="/search/physics?searchtype=author&query=He%2C+C">Cong He</a>, <a href="/search/physics?searchtype=author&query=Hu%2C+J">Jun Hu</a>, <a href="/search/physics?searchtype=author&query=Isocrate%2C+R">Roberto Isocrate</a>, <a href="/search/physics?searchtype=author&query=Jelmini%2C+B">Beatrice Jelmini</a>, <a href="/search/physics?searchtype=author&query=Ji%2C+X">Xiaolu Ji</a>, <a href="/search/physics?searchtype=author&query=Jiang%2C+X">Xiaoshan Jiang</a> , et al. (105 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.08454v1-abstract-short" style="display: inline;"> The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.08454v1-abstract-full').style.display = 'inline'; document.getElementById('2212.08454v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.08454v1-abstract-full" style="display: none;"> The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.08454v1-abstract-full').style.display = 'none'; document.getElementById('2212.08454v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 13 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.14104">arXiv:2210.14104</a> <span> [<a href="https://arxiv.org/pdf/2210.14104">pdf</a>, <a href="https://arxiv.org/format/2210.14104">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1021/acs.jpclett.2c03327">10.1021/acs.jpclett.2c03327 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Skipping the Replica Exchange Ladder with Normalizing Flows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Invernizzi%2C+M">Michele Invernizzi</a>, <a href="/search/physics?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.14104v2-abstract-short" style="display: inline;"> We combine replica exchange (parallel tempering) with normalizing flows, a class of deep generative models. These two sampling strategies complement each other, resulting in an efficient strategy for sampling molecular systems characterized by rare events, which we call learned replica exchange (LREX). In LREX, a normalizing flow is trained to map the configurations of the fastest-mixing replica i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14104v2-abstract-full').style.display = 'inline'; document.getElementById('2210.14104v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.14104v2-abstract-full" style="display: none;"> We combine replica exchange (parallel tempering) with normalizing flows, a class of deep generative models. These two sampling strategies complement each other, resulting in an efficient strategy for sampling molecular systems characterized by rare events, which we call learned replica exchange (LREX). In LREX, a normalizing flow is trained to map the configurations of the fastest-mixing replica into configurations belonging to the target distribution, allowing direct exchanges between the two without the need to simulate intermediate replicas. This can significantly reduce the computational cost compared to standard replica exchange. The proposed method also offers several advantages with respect to Boltzmann generators that directly use normalizing flows to sample the target distribution. We apply LREX to some prototypical molecular dynamics systems, highlighting the improvements over previous methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.14104v2-abstract-full').style.display = 'none'; document.getElementById('2210.14104v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.06205">arXiv:2208.06205</a> <span> [<a href="https://arxiv.org/pdf/2208.06205">pdf</a>, <a href="https://arxiv.org/format/2208.06205">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistical Mechanics">cond-mat.stat-mech</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/5.0120386">10.1063/5.0120386 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Quantum dynamics using path integral coarse-graining </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Musil%2C+F">F茅lix Musil</a>, <a href="/search/physics?searchtype=author&query=Zaporozhets%2C+I">Iryna Zaporozhets</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=Kapil%2C+V">Venkat Kapil</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.06205v2-abstract-short" style="display: inline;"> Vibrational spectra of condensed and gas-phase systems containing light nuclei are influenced by their quantum-mechanical behaviour. The quantum dynamics of light nuclei can be approximated by the imaginary time path integral (PI) formulation, but still at a large computational cost that increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06205v2-abstract-full').style.display = 'inline'; document.getElementById('2208.06205v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.06205v2-abstract-full" style="display: none;"> Vibrational spectra of condensed and gas-phase systems containing light nuclei are influenced by their quantum-mechanical behaviour. The quantum dynamics of light nuclei can be approximated by the imaginary time path integral (PI) formulation, but still at a large computational cost that increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop a PI method with the reduced computational cost of a classical simulation. We also propose a simple temperature elevation scheme to significantly attenuate the artefacts of standard PI approaches and also eliminate the unfavourable temperature scaling of the computational cost.We illustrate the approach, by calculating vibrational spectra using standard models of water molecules and bulk water, demonstrating significant computational savings and dramatically improved accuracy compared to more expensive reference approaches. We believe that our simple, efficient and accurate method could enable routine calculations of vibrational spectra including nuclear quantum effects for a wide range of molecular systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06205v2-abstract-full').style.display = 'none'; document.getElementById('2208.06205v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages; 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.08830">arXiv:2205.08830</a> <span> [<a href="https://arxiv.org/pdf/2205.08830">pdf</a>, <a href="https://arxiv.org/format/2205.08830">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/1475-7516/2022/10/033">10.1088/1475-7516/2022/10/033 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Prospects for Detecting the Diffuse Supernova Neutrino Background with JUNO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+Collaboration"> JUNO Collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Balashov%2C+N">Nikita Balashov</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">Davide Basilico</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Birkenfeld%2C+T">Thilo Birkenfeld</a>, <a href="/search/physics?searchtype=author&query=Blin%2C+S">Sylvie Blin</a> , et al. (577 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.08830v2-abstract-short" style="display: inline;"> We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced n… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08830v2-abstract-full').style.display = 'inline'; document.getElementById('2205.08830v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.08830v2-abstract-full" style="display: none;"> We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced neutral current (NC) background turns out to be the most critical background, whose uncertainty is carefully evaluated from both the spread of model predictions and an envisaged \textit{in situ} measurement. We also make a careful study on the background suppression with the pulse shape discrimination (PSD) and triple coincidence (TC) cuts. With latest DSNB signal predictions, more realistic background evaluation and PSD efficiency optimization, and additional TC cut, JUNO can reach the significance of 3$蟽$ for 3 years of data taking, and achieve better than 5$蟽$ after 10 years for a reference DSNB model. In the pessimistic scenario of non-observation, JUNO would strongly improve the limits and exclude a significant region of the model parameter space. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08830v2-abstract-full').style.display = 'none'; document.getElementById('2205.08830v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 11 figures, final published version in JCAP</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> JCAP 10 (2022) 033 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.08629">arXiv:2205.08629</a> <span> [<a href="https://arxiv.org/pdf/2205.08629">pdf</a>, <a href="https://arxiv.org/format/2205.08629">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1140/epjc/s10052-022-11002-8">10.1140/epjc/s10052-022-11002-8 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Mass Testing and Characterization of 20-inch PMTs for JUNO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=Aleem%2C+A">Abid Aleem</a>, <a href="/search/physics?searchtype=author&query=Alexandros%2C+T">Tsagkarakis Alexandros</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andre%2C+J+P+A+M">Joao Pedro Athayde Marcondes de Andre</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Bai%2C+W">Weidong Bai</a>, <a href="/search/physics?searchtype=author&query=Balashov%2C+N">Nikita Balashov</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">Davide Basilico</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a> , et al. (541 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.08629v2-abstract-short" style="display: inline;"> Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3 % at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program whic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08629v2-abstract-full').style.display = 'inline'; document.getElementById('2205.08629v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.08629v2-abstract-full" style="display: none;"> Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3 % at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5,000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.08629v2-abstract-full').style.display = 'none'; document.getElementById('2205.08629v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.11167">arXiv:2203.11167</a> <span> [<a href="https://arxiv.org/pdf/2203.11167">pdf</a>, <a href="https://arxiv.org/format/2203.11167">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1021/acs.jctc.3c00016">10.1021/acs.jctc.3c00016 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Flow-matching -- efficient coarse-graining of molecular dynamics without forces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=K%C3%B6hler%2C+J">Jonas K枚hler</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Y">Yaoyi Chen</a>, <a href="/search/physics?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.11167v4-abstract-short" style="display: inline;"> Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11167v4-abstract-full').style.display = 'inline'; document.getElementById('2203.11167v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.11167v4-abstract-full" style="display: none;"> Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we present flow-matching, a new training method for CG force fields that combines the advantages of both methods by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density, which is equivalent to minimizing the relative entropy without requiring iterative CG simulations. Subsequently, the flow generates samples and forces according to the learned distribution in order to train the desired CG free energy model via force matching. Even without requiring forces from the all-atom simulations, flow-matching outperforms classical force-matching by an order of magnitude in terms of data efficiency, and produces CG models that can capture the folding and unfolding transitions of small proteins. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11167v4-abstract-full').style.display = 'none'; document.getElementById('2203.11167v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> J. Chem. Theory Comput. 2023, XXXX, XXX, XXX-XXX </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.03669">arXiv:2107.03669</a> <span> [<a href="https://arxiv.org/pdf/2107.03669">pdf</a>, <a href="https://arxiv.org/format/2107.03669">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/JHEP11(2021)102">10.1007/JHEP11(2021)102 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Radioactivity control strategy for the JUNO detector </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+collaboration"> JUNO collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Babic%2C+A">Andrej Babic</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">Davide Basilico</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Birkenfeld%2C+T">Thilo Birkenfeld</a>, <a href="/search/physics?searchtype=author&query=Blin%2C+S">Sylvie Blin</a> , et al. (578 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.03669v2-abstract-short" style="display: inline;"> JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particula… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.03669v2-abstract-full').style.display = 'inline'; document.getElementById('2107.03669v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.03669v2-abstract-full" style="display: none;"> JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz in the default fiducial volume, above an energy threshold of 0.7 MeV. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.03669v2-abstract-full').style.display = 'none'; document.getElementById('2107.03669v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">35 pages, 12 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.07492">arXiv:2106.07492</a> <span> [<a href="https://arxiv.org/pdf/2106.07492">pdf</a>, <a href="https://arxiv.org/format/2106.07492">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/5.0059915">10.1063/5.0059915 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Machine Learning Implicit Solvation for Molecular Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Chen%2C+Y">Yaoyi Chen</a>, <a href="/search/physics?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/physics?searchtype=author&query=Charron%2C+N+E">Nicholas E. Charron</a>, <a href="/search/physics?searchtype=author&query=Husic%2C+B+E">Brooke E. Husic</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.07492v2-abstract-short" style="display: inline;"> Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or ce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.07492v2-abstract-full').style.display = 'inline'; document.getElementById('2106.07492v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.07492v2-abstract-full" style="display: none;"> Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models, as the many-body effects of the neglected solvent molecules is difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML--CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to MD simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely-used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.07492v2-abstract-full').style.display = 'none'; document.getElementById('2106.07492v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 6 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> J. Chem. Phys. 155, 084101 (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.16900">arXiv:2103.16900</a> <span> [<a href="https://arxiv.org/pdf/2103.16900">pdf</a>, <a href="https://arxiv.org/format/2103.16900">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> The Design and Sensitivity of JUNO's scintillator radiopurity pre-detector OSIRIS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+Collaboration"> JUNO Collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+G">Guangpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Babic%2C+A">Andrej Babic</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Basilico%2C+D">Davide Basilico</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Birkenfeld%2C+T">Thilo Birkenfeld</a> , et al. (582 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.16900v1-abstract-short" style="display: inline;"> The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $10^{-16}$ g/g of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.16900v1-abstract-full').style.display = 'inline'; document.getElementById('2103.16900v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.16900v1-abstract-full" style="display: none;"> The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $10^{-16}$ g/g of $^{238}$U and $^{232}$Th requires a large ($\sim$20 m$^3$) detection volume and ultralow background levels. The present paper reports on the design and major components of the OSIRIS detector, the detector simulation as well as the measuring strategies foreseen and the sensitivity levels to U/Th that can be reached in this setup. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.16900v1-abstract-full').style.display = 'none'; document.getElementById('2103.16900v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">32 pages, 22 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.12106">arXiv:2012.12106</a> <span> [<a href="https://arxiv.org/pdf/2012.12106">pdf</a>, <a href="https://arxiv.org/format/2012.12106">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1021/acs.jctc.0c01343">10.1021/acs.jctc.0c01343 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> TorchMD: A deep learning framework for molecular simulations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Doerr%2C+S">Stefan Doerr</a>, <a href="/search/physics?searchtype=author&query=Majewsk%2C+M">Maciej Majewsk</a>, <a href="/search/physics?searchtype=author&query=P%C3%A9rez%2C+A">Adri脿 P茅rez</a>, <a href="/search/physics?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=Noe%2C+F">Frank Noe</a>, <a href="/search/physics?searchtype=author&query=Giorgino%2C+T">Toni Giorgino</a>, <a href="/search/physics?searchtype=author&query=De+Fabritiis%2C+G">Gianni De Fabritiis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.12106v1-abstract-short" style="display: inline;"> Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations inc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.12106v1-abstract-full').style.display = 'inline'; document.getElementById('2012.12106v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.12106v1-abstract-full" style="display: none;"> Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool-set to support molecular simulations of machine learning potentials. Code and data are freely available at \url{github.com/torchmd}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.12106v1-abstract-full').style.display = 'none'; document.getElementById('2012.12106v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.06405">arXiv:2011.06405</a> <span> [<a href="https://arxiv.org/pdf/2011.06405">pdf</a>, <a href="https://arxiv.org/format/2011.06405">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/JHEP03(2021)004">10.1007/JHEP03(2021)004 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Calibration Strategy of the JUNO Experiment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+collaboration"> JUNO collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Ahmed%2C+R">Rizwan Ahmed</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+G">Guangpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Babic%2C+A">Andrej Babic</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Bernieri%2C+E">Enrico Bernieri</a>, <a href="/search/physics?searchtype=author&query=Birkenfeld%2C+T">Thilo Birkenfeld</a> , et al. (571 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.06405v3-abstract-short" style="display: inline;"> We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector ca… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.06405v3-abstract-full').style.display = 'inline'; document.getElementById('2011.06405v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.06405v3-abstract-full" style="display: none;"> We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector can achieve a better than 1% energy linearity and a 3% effective energy resolution, required by the neutrino mass ordering determination. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.06405v3-abstract-full').style.display = 'none'; document.getElementById('2011.06405v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.05414">arXiv:2008.05414</a> <span> [<a href="https://arxiv.org/pdf/2008.05414">pdf</a>, <a href="https://arxiv.org/format/2008.05414">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1098/rsta.2020.0068">10.1098/rsta.2020.0068 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Reproducible Validation and Replication Studies in Nanoscale Physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Clementi%2C+N+C">Natalia C. Clementi</a>, <a href="/search/physics?searchtype=author&query=Barba%2C+L+A">Lorena A. Barba</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.05414v1-abstract-short" style="display: inline;"> Credibility building activities in computational research include verification and validation, reproducibility and replication, and uncertainty quantification. Though orthogonal to each other, they are related. This paper presents validation and replication studies in electromagnetic excitations on nanoscale structures, where the quantity of interest is the wavelength at which resonance peaks occu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.05414v1-abstract-full').style.display = 'inline'; document.getElementById('2008.05414v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.05414v1-abstract-full" style="display: none;"> Credibility building activities in computational research include verification and validation, reproducibility and replication, and uncertainty quantification. Though orthogonal to each other, they are related. This paper presents validation and replication studies in electromagnetic excitations on nanoscale structures, where the quantity of interest is the wavelength at which resonance peaks occur. The study uses the open-source software PyGBe: a boundary element solver with trecode acceleration and GPU capability. We replicate a result by Rockstuhl et al. (2005, doi:10/dsxw9d) with a two-dimensional boundary element method on silicon carbide particles, despite differences in our method. The second replication case from Ellis et al. (2016, doi:10/f83zcb) looks at aspect ratio effects on high-order modes of localized surface phonon-polariton nanostructures. The results partially replicate: the wavenumber position of some mode match, but for other modes they differ. With virtually no information about the original simulations, explaining the discrepancies is not possible. A comparison with experiments that measured polarized reflectance of silicon carbide nano pillars provides a validation case. The wavenumber of the dominant mode and two more do match, but differences remain in other minor modes. Results in this paper were produced with strict reproducibility practices, and we share reproducibility packages for all, including input files, execution scripts, secondary data, post-processing code and plotting scripts, and the figures (deposited in Zenodo). In view of the many challenges faced, we propose that reproducible practices make replication and validation more feasible. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.05414v1-abstract-full').style.display = 'none'; document.getElementById('2008.05414v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.11412">arXiv:2007.11412</a> <span> [<a href="https://arxiv.org/pdf/2007.11412">pdf</a>, <a href="https://arxiv.org/format/2007.11412">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/5.0026133">10.1063/5.0026133 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Coarse Graining Molecular Dynamics with Graph Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Husic%2C+B+E">Brooke E. Husic</a>, <a href="/search/physics?searchtype=author&query=Charron%2C+N+E">Nicholas E. Charron</a>, <a href="/search/physics?searchtype=author&query=Lemm%2C+D">Dominik Lemm</a>, <a href="/search/physics?searchtype=author&query=Wang%2C+J">Jiang Wang</a>, <a href="/search/physics?searchtype=author&query=P%C3%A9rez%2C+A">Adri脿 P茅rez</a>, <a href="/search/physics?searchtype=author&query=Majewski%2C+M">Maciej Majewski</a>, <a href="/search/physics?searchtype=author&query=Kr%C3%A4mer%2C+A">Andreas Kr盲mer</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Y">Yaoyi Chen</a>, <a href="/search/physics?searchtype=author&query=Olsson%2C+S">Simon Olsson</a>, <a href="/search/physics?searchtype=author&query=de+Fabritiis%2C+G">Gianni de Fabritiis</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.11412v3-abstract-short" style="display: inline;"> Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proven that a force matching scheme defines a thermodyna… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.11412v3-abstract-full').style.display = 'inline'; document.getElementById('2007.11412v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.11412v3-abstract-full" style="display: none;"> Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proven that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features upon which to machine learn the force field. In the present contribution, we build upon the advance of Wang et al.and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learns their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.11412v3-abstract-full').style.display = 'none'; document.getElementById('2007.11412v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.11760">arXiv:2006.11760</a> <span> [<a href="https://arxiv.org/pdf/2006.11760">pdf</a>, <a href="https://arxiv.org/format/2006.11760">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Feasibility and physics potential of detecting $^8$B solar neutrinos at JUNO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+collaboration"> JUNO collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=Ali%2C+N">Nawab Ali</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+G">Guangpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Babic%2C+A">Andrej Babic</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Bernieri%2C+E">Enrico Bernieri</a>, <a href="/search/physics?searchtype=author&query=Biare%2C+D">David Biare</a> , et al. (572 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.11760v1-abstract-short" style="display: inline;"> The Jiangmen Underground Neutrino Observatory~(JUNO) features a 20~kt multi-purpose underground liquid scintillator sphere as its main detector. Some of JUNO's features make it an excellent experiment for $^8$B solar neutrino measurements, such as its low-energy threshold, its high energy resolution compared to water Cherenkov detectors, and its much large target mass compared to previous liquid s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.11760v1-abstract-full').style.display = 'inline'; document.getElementById('2006.11760v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.11760v1-abstract-full" style="display: none;"> The Jiangmen Underground Neutrino Observatory~(JUNO) features a 20~kt multi-purpose underground liquid scintillator sphere as its main detector. Some of JUNO's features make it an excellent experiment for $^8$B solar neutrino measurements, such as its low-energy threshold, its high energy resolution compared to water Cherenkov detectors, and its much large target mass compared to previous liquid scintillator detectors. In this paper we present a comprehensive assessment of JUNO's potential for detecting $^8$B solar neutrinos via the neutrino-electron elastic scattering process. A reduced 2~MeV threshold on the recoil electron energy is found to be achievable assuming the intrinsic radioactive background $^{238}$U and $^{232}$Th in the liquid scintillator can be controlled to 10$^{-17}$~g/g. With ten years of data taking, about 60,000 signal and 30,000 background events are expected. This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter, which will shed new light on the tension between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework. If $螖m^{2}_{21}=4.8\times10^{-5}~(7.5\times10^{-5})$~eV$^{2}$, JUNO can provide evidence of neutrino oscillation in the Earth at the about 3$蟽$~(2$蟽$) level by measuring the non-zero signal rate variation with respect to the solar zenith angle. Moveover, JUNO can simultaneously measure $螖m^2_{21}$ using $^8$B solar neutrinos to a precision of 20\% or better depending on the central value and to sub-percent precision using reactor antineutrinos. A comparison of these two measurements from the same detector will help elucidate the current tension between the value of $螖m^2_{21}$ reported by solar neutrino experiments and the KamLAND experiment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.11760v1-abstract-full').style.display = 'none'; document.getElementById('2006.11760v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 14 plots, 7 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.08745">arXiv:2005.08745</a> <span> [<a href="https://arxiv.org/pdf/2005.08745">pdf</a>, <a href="https://arxiv.org/format/2005.08745">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> </div> </div> <p class="title is-5 mathjax"> TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=JUNO+Collaboration"> JUNO Collaboration</a>, <a href="/search/physics?searchtype=author&query=Abusleme%2C+A">Angel Abusleme</a>, <a href="/search/physics?searchtype=author&query=Adam%2C+T">Thomas Adam</a>, <a href="/search/physics?searchtype=author&query=Ahmad%2C+S">Shakeel Ahmad</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">Sebastiano Aiello</a>, <a href="/search/physics?searchtype=author&query=Akram%2C+M">Muhammad Akram</a>, <a href="/search/physics?searchtype=author&query=Ali%2C+N">Nawab Ali</a>, <a href="/search/physics?searchtype=author&query=An%2C+F">Fengpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+G">Guangpeng An</a>, <a href="/search/physics?searchtype=author&query=An%2C+Q">Qi An</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">Giuseppe Andronico</a>, <a href="/search/physics?searchtype=author&query=Anfimov%2C+N">Nikolay Anfimov</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">Vito Antonelli</a>, <a href="/search/physics?searchtype=author&query=Antoshkina%2C+T">Tatiana Antoshkina</a>, <a href="/search/physics?searchtype=author&query=Asavapibhop%2C+B">Burin Asavapibhop</a>, <a href="/search/physics?searchtype=author&query=de+Andr%C3%A9%2C+J+P+A+M">Jo茫o Pedro Athayde Marcondes de Andr茅</a>, <a href="/search/physics?searchtype=author&query=Auguste%2C+D">Didier Auguste</a>, <a href="/search/physics?searchtype=author&query=Babic%2C+A">Andrej Babic</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">Wander Baldini</a>, <a href="/search/physics?searchtype=author&query=Barresi%2C+A">Andrea Barresi</a>, <a href="/search/physics?searchtype=author&query=Baussan%2C+E">Eric Baussan</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">Marco Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">Antonio Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Bernieri%2C+E">Enrico Bernieri</a>, <a href="/search/physics?searchtype=author&query=Biare%2C+D">David Biare</a> , et al. (568 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.08745v1-abstract-short" style="display: inline;"> The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.08745v1-abstract-full').style.display = 'inline'; document.getElementById('2005.08745v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.08745v1-abstract-full" style="display: none;"> The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.08745v1-abstract-full').style.display = 'none'; document.getElementById('2005.08745v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">134 pages, 114 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.01851">arXiv:2005.01851</a> <span> [<a href="https://arxiv.org/pdf/2005.01851">pdf</a>, <a href="https://arxiv.org/format/2005.01851">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/5.0007276">10.1063/5.0007276 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Wang%2C+J">Jiang Wang</a>, <a href="/search/physics?searchtype=author&query=Chmiela%2C+S">Stefan Chmiela</a>, <a href="/search/physics?searchtype=author&query=M%C3%BCller%2C+K">Klaus-Robert M眉ller</a>, <a href="/search/physics?searchtype=author&query=No%C3%A8%2C+F">Frank No猫</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.01851v1-abstract-short" style="display: inline;"> Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorithm. Here, we demonstrate its application to learn an effective coarse-grained (CG) model from all-atom simulation data in a sample efficient manner. The coarse-grained force field is learned by following the thermodynami… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01851v1-abstract-full').style.display = 'inline'; document.getElementById('2005.01851v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.01851v1-abstract-full" style="display: none;"> Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorithm. Here, we demonstrate its application to learn an effective coarse-grained (CG) model from all-atom simulation data in a sample efficient manner. The coarse-grained force field is learned by following the thermodynamic consistency principle, here by minimizing the error between the predicted coarse-grained force and the all-atom mean force in the coarse-grained coordinates. Solving this problem by GDML directly is impossible because coarse-graining requires averaging over many training data points, resulting in impractical memory requirements for storing the kernel matrices. In this work, we propose a data-efficient and memory-saving alternative. Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective coarse-grained model. We illustrate our method on a simple biomolecular system, alanine dipeptide, by reconstructing the free energy landscape of a coarse-grained variant of this molecule. Our novel GDML training scheme yields a smaller free energy error than neural networks when the training set is small, and a comparably high accuracy when the training set is sufficiently large. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.01851v1-abstract-full').style.display = 'none'; document.getElementById('2005.01851v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.08339">arXiv:2003.08339</a> <span> [<a href="https://arxiv.org/pdf/2003.08339">pdf</a>, <a href="https://arxiv.org/format/2003.08339">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2020.164600">10.1016/j.nima.2020.164600 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Embedded Readout Electronics R&D for the Large PMTs in the JUNO Experiment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Bellato%2C+M">M. Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">A. Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+A">A. Brugnera</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+S">S. Chen</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Z">Z. Chen</a>, <a href="/search/physics?searchtype=author&query=Clerbaux%2C+B">B. Clerbaux</a>, <a href="/search/physics?searchtype=author&query=Corso%2C+F+d">F. dal Corso</a>, <a href="/search/physics?searchtype=author&query=Corti%2C+D">D. Corti</a>, <a href="/search/physics?searchtype=author&query=Dong%2C+J">J. Dong</a>, <a href="/search/physics?searchtype=author&query=Galet%2C+G">G. Galet</a>, <a href="/search/physics?searchtype=author&query=Garfagnini%2C+A">A. Garfagnini</a>, <a href="/search/physics?searchtype=author&query=Giaz%2C+A">A. Giaz</a>, <a href="/search/physics?searchtype=author&query=Gong%2C+G">G. Gong</a>, <a href="/search/physics?searchtype=author&query=Grewing%2C+C">C. Grewing</a>, <a href="/search/physics?searchtype=author&query=Hu%2C+J">J. Hu</a>, <a href="/search/physics?searchtype=author&query=Isocrate%2C+R">R. Isocrate</a>, <a href="/search/physics?searchtype=author&query=Jiang%2C+X">X. Jiang</a>, <a href="/search/physics?searchtype=author&query=Li%2C+F">F. Li</a>, <a href="/search/physics?searchtype=author&query=Lippi%2C+I">I. Lippi</a>, <a href="/search/physics?searchtype=author&query=Marini%2C+F">F. Marini</a>, <a href="/search/physics?searchtype=author&query=Ning%2C+Z">Z. Ning</a>, <a href="/search/physics?searchtype=author&query=Olshevskiyi%2C+A+G">A. G. Olshevskiyi</a>, <a href="/search/physics?searchtype=author&query=Pedretti%2C+D">D. Pedretti</a>, <a href="/search/physics?searchtype=author&query=Petitjean%2C+P+A">P. A. Petitjean</a>, <a href="/search/physics?searchtype=author&query=Robens%2C+M">M. Robens</a> , et al. (69 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.08339v2-abstract-short" style="display: inline;"> Jiangmen Underground neutrino Observatory (JUNO) is a next generation liquid scintillator neutrino experiment under construction phase in South China. Thanks to the anti-neutrinos produced by the nearby nuclear power plants, JUNO will primarily study the neutrino mass hierarchy, one of the open key questions in neutrino physics. One key ingredient for the success of the measurement is to use high… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.08339v2-abstract-full').style.display = 'inline'; document.getElementById('2003.08339v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.08339v2-abstract-full" style="display: none;"> Jiangmen Underground neutrino Observatory (JUNO) is a next generation liquid scintillator neutrino experiment under construction phase in South China. Thanks to the anti-neutrinos produced by the nearby nuclear power plants, JUNO will primarily study the neutrino mass hierarchy, one of the open key questions in neutrino physics. One key ingredient for the success of the measurement is to use high speed, high resolution sampling electronics located very close to the detector signal. Linearity in the response of the electronics in another important ingredient for the success of the experiment. During the initial design phase of the electronics, a custom design, with the Front-End and Read-Out electronics located very close to the detector analog signal has been developed and successfully tested. The present paper describes the electronics structure and the first tests performed on the prototypes. The electronics prototypes have been tested and they show good linearity response, with a maximum deviation of 1.3% over the full dynamic range (1-1000 p.e.), fulfilling the JUNO experiment requirements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.08339v2-abstract-full').style.display = 'none'; document.getElementById('2003.08339v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 15 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.09811">arXiv:1911.09811</a> <span> [<a href="https://arxiv.org/pdf/1911.09811">pdf</a>, <a href="https://arxiv.org/format/1911.09811">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Machine learning for protein folding and dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a>, <a href="/search/physics?searchtype=author&query=De+Fabritiis%2C+G">Gianni De Fabritiis</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.09811v1-abstract-short" style="display: inline;"> Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way simulations are performed to explore the energy landscape of protein systems is also changing as force-fields are started to be designed by means of mac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.09811v1-abstract-full').style.display = 'inline'; document.getElementById('1911.09811v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.09811v1-abstract-full" style="display: none;"> Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way simulations are performed to explore the energy landscape of protein systems is also changing as force-fields are started to be designed by means of machine learning methods. These methods are also used to extract the essential information from large simulation datasets and to enhance the sampling of rare events such as folding/unfolding transitions. While significant challenges still need to be tackled, we expect these methods to play an important role on the study of protein folding and dynamics in the near future. We discuss here the recent advances on all these fronts and the questions that need to be addressed for machine learning approaches to become mainstream in protein simulation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.09811v1-abstract-full').style.display = 'none'; document.getElementById('1911.09811v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.04836">arXiv:1911.04836</a> <span> [<a href="https://arxiv.org/pdf/1911.04836">pdf</a>, <a href="https://arxiv.org/format/1911.04836">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> $^{222}$Rn contamination mechanisms on acrylic surfaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Nastasi%2C+M">M. Nastasi</a>, <a href="/search/physics?searchtype=author&query=Paonessa%2C+A">A. Paonessa</a>, <a href="/search/physics?searchtype=author&query=Previtali%2C+E">E. Previtali</a>, <a href="/search/physics?searchtype=author&query=Quadrivi%2C+E">E. Quadrivi</a>, <a href="/search/physics?searchtype=author&query=Sisti%2C+M">M. Sisti</a>, <a href="/search/physics?searchtype=author&query=Aiello%2C+S">S. Aiello</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">G. Andronico</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">V. Antonelli</a>, <a href="/search/physics?searchtype=author&query=Baldini%2C+W">W. Baldini</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">M. Bellato</a>, <a href="/search/physics?searchtype=author&query=Bergnoli%2C+A">A. Bergnoli</a>, <a href="/search/physics?searchtype=author&query=Brigatti%2C+A">A. Brigatti</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">R. Brugnera</a>, <a href="/search/physics?searchtype=author&query=Budano%2C+A">A. Budano</a>, <a href="/search/physics?searchtype=author&query=Buscemi%2C+M">M. Buscemi</a>, <a href="/search/physics?searchtype=author&query=Cammi%2C+A">A. Cammi</a>, <a href="/search/physics?searchtype=author&query=Caruso%2C+R">R. Caruso</a>, <a href="/search/physics?searchtype=author&query=Chiesa%2C+D">D. Chiesa</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">C. Clementi</a>, <a href="/search/physics?searchtype=author&query=Corti%2C+D">D. Corti</a>, <a href="/search/physics?searchtype=author&query=Costa%2C+S">S. Costa</a>, <a href="/search/physics?searchtype=author&query=Corso%2C+F+D">F. Dal Corso</a>, <a href="/search/physics?searchtype=author&query=Ding%2C+X+F">X. F. Ding</a>, <a href="/search/physics?searchtype=author&query=Dusini%2C+S">S. Dusini</a>, <a href="/search/physics?searchtype=author&query=Fabbri%2C+A">A. Fabbri</a> , et al. (42 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.04836v1-abstract-short" style="display: inline;"> In this work, the $^{222}$Rn contamination mechanisms on acrylic surfaces have been investigated. $^{222}$Rn can represent a significant background source for low-background experiments, and acrylic is a suitable material for detector design thanks to its purity and transparency. Four acrylic samples have been exposed to a $^{222}$Rn rich environment for different time periods, being contaminated… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.04836v1-abstract-full').style.display = 'inline'; document.getElementById('1911.04836v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.04836v1-abstract-full" style="display: none;"> In this work, the $^{222}$Rn contamination mechanisms on acrylic surfaces have been investigated. $^{222}$Rn can represent a significant background source for low-background experiments, and acrylic is a suitable material for detector design thanks to its purity and transparency. Four acrylic samples have been exposed to a $^{222}$Rn rich environment for different time periods, being contaminated by $^{222}$Rn and its progenies. Subsequently, the time evolution of radiocontaminants activity on the samples has been evaluated with $伪$ and $纬$ measurements, highlighting the role of different decay modes in the contamination process. A detailed analysis of the alpha spectra allowed to quantify the implantation depth of the contaminants. Moreover, a study of both $伪$ and $纬$ measurements pointed out the $^{222}$Rn diffusion inside the samples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.04836v1-abstract-full').style.display = 'none'; document.getElementById('1911.04836v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.02792">arXiv:1911.02792</a> <span> [<a href="https://arxiv.org/pdf/1911.02792">pdf</a>, <a href="https://arxiv.org/format/1911.02792">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1146/annurev-physchem-042018-052331">10.1146/annurev-physchem-042018-052331 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Machine learning for molecular simulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a>, <a href="/search/physics?searchtype=author&query=Tkatchenko%2C+A">Alexandre Tkatchenko</a>, <a href="/search/physics?searchtype=author&query=M%C3%BCller%2C+K">Klaus-Robert M眉ller</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.02792v1-abstract-short" style="display: inline;"> Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.02792v1-abstract-full').style.display = 'inline'; document.getElementById('1911.02792v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.02792v1-abstract-full" style="display: none;"> Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, coarse-grained molecular dynamics, the extraction of free energy surfaces and kinetics and generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into machine learning structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.02792v1-abstract-full').style.display = 'none'; document.getElementById('1911.02792v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> No茅 F, Tkatchenko A, M眉ller KR, Clementi C. 2020. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.04741">arXiv:1908.04741</a> <span> [<a href="https://arxiv.org/pdf/1908.04741">pdf</a>, <a href="https://arxiv.org/format/1908.04741">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Tensor-based computation of metastable and coherent sets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=N%C3%BCske%2C+F">Feliks N眉ske</a>, <a href="/search/physics?searchtype=author&query=Gel%C3%9F%2C+P">Patrick Gel脽</a>, <a href="/search/physics?searchtype=author&query=Klus%2C+S">Stefan Klus</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1908.04741v3-abstract-short" style="display: inline;"> Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches. On the other hand, low-rank tensor product approximations -- in particular the tensor train (TT) format -- have become a valuable tool for the solution of large-scale problems in a number of fields. In this work, we combine Koopman-based models and the TT… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.04741v3-abstract-full').style.display = 'inline'; document.getElementById('1908.04741v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.04741v3-abstract-full" style="display: none;"> Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches. On the other hand, low-rank tensor product approximations -- in particular the tensor train (TT) format -- have become a valuable tool for the solution of large-scale problems in a number of fields. In this work, we combine Koopman-based models and the TT format, enabling their application to high-dimensional problems in conjunction with a rich set of basis functions or features. We derive efficient algorithms to obtain a reduced matrix representation of the system's evolution operator starting from an appropriate low-rank representation of the data. These algorithms can be applied to both stationary and non-stationary systems. We establish the infinite-data limit of these matrix representations, and demonstrate our methods' capabilities using several benchmark data sets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.04741v3-abstract-full').style.display = 'none'; document.getElementById('1908.04741v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1907.06954">arXiv:1907.06954</a> <span> [<a href="https://arxiv.org/pdf/1907.06954">pdf</a>, <a href="https://arxiv.org/format/1907.06954">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Extensible and Scalable Adaptive Sampling on Supercomputers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Hruska%2C+E">Eugen Hruska</a>, <a href="/search/physics?searchtype=author&query=Balasubramanian%2C+V">Vivekanandan Balasubramanian</a>, <a href="/search/physics?searchtype=author&query=Lee%2C+H">Hyungro Lee</a>, <a href="/search/physics?searchtype=author&query=Jha%2C+S">Shantenu Jha</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1907.06954v2-abstract-short" style="display: inline;"> The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of High-Performance Computers (HPC) systems. Utilizing only "brute force" MD simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy the speed up can be more tha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.06954v2-abstract-full').style.display = 'inline'; document.getElementById('1907.06954v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1907.06954v2-abstract-full" style="display: none;"> The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of High-Performance Computers (HPC) systems. Utilizing only "brute force" MD simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy the speed up can be more than one order of magnitude. One challenge limiting the utilization of adaptive sampling by domain experts is the relatively high complexity of efficiently running adaptive sampling on HPC systems. We discuss how the ExTASY framework can set up new adaptive sampling strategies, and reliably execute resulting workflows at scale on HPC platforms. Here the folding dynamics of four proteins are predicted with no a priori information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.06954v2-abstract-full').style.display = 'none'; document.getElementById('1907.06954v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 July, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.07177">arXiv:1904.07177</a> <span> [<a href="https://arxiv.org/pdf/1904.07177">pdf</a>, <a href="https://arxiv.org/format/1904.07177">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/1.5100131">10.1063/1.5100131 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Coarse-graining Molecular Systems by Spectral Matching </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=N%C3%BCske%2C+F">Feliks N眉ske</a>, <a href="/search/physics?searchtype=author&query=Boninsegna%2C+L">Lorenzo Boninsegna</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.07177v1-abstract-short" style="display: inline;"> Coarse-graining has become an area of tremendous importance within many different research fields. For molecular simulation, coarse-graining bears the promise of finding simplified models such that long-time simulations of large-scale systems become computationally tractable. While significant progress has been made in tuning thermodynamic properties of reduced models, it remains a key challenge t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.07177v1-abstract-full').style.display = 'inline'; document.getElementById('1904.07177v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.07177v1-abstract-full" style="display: none;"> Coarse-graining has become an area of tremendous importance within many different research fields. For molecular simulation, coarse-graining bears the promise of finding simplified models such that long-time simulations of large-scale systems become computationally tractable. While significant progress has been made in tuning thermodynamic properties of reduced models, it remains a key challenge to ensure that relevant kinetic properties are retained by coarse-grained dynamical systems. In this study, we focus on data-driven methods to preserve the rare-event kinetics of the original system, and make use of their close connection to the low-lying spectrum of the system's generator. Building on work by Crommelin and Vanden-Eijnden, SIAM Multiscale Model. Simul. (2011), we present a general framework, called spectral matching, which directly targets the generator's leading eigenvalue equations when learning parameters for coarse-grained models. We discuss different parametric models for effective dynamics and derive the resulting data-based regression problems. We show that spectral matching can be used to learn effective potentials which retain the slow dynamics, but also to correct the dynamics induced by existing techniques, such as force matching. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.07177v1-abstract-full').style.display = 'none'; document.getElementById('1904.07177v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.05288">arXiv:1902.05288</a> <span> [<a href="https://arxiv.org/pdf/1902.05288">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2019.01.071">10.1016/j.nima.2019.01.071 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Distillation and stripping pilot plants for the JUNO neutrino detector: design, operations and reliability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Lombardi%2C+P">P. Lombardi</a>, <a href="/search/physics?searchtype=author&query=Montuschi%2C+M">M. Montuschi</a>, <a href="/search/physics?searchtype=author&query=Formozov%2C+A">A. Formozov</a>, <a href="/search/physics?searchtype=author&query=Brigatti%2C+A">A. Brigatti</a>, <a href="/search/physics?searchtype=author&query=Parmeggiano%2C+S">S. Parmeggiano</a>, <a href="/search/physics?searchtype=author&query=Pompilio%2C+R">R. Pompilio</a>, <a href="/search/physics?searchtype=author&query=Depnering%2C+W">W. Depnering</a>, <a href="/search/physics?searchtype=author&query=Franke%2C+S">S. Franke</a>, <a href="/search/physics?searchtype=author&query=Gaigher%2C+R">R. Gaigher</a>, <a href="/search/physics?searchtype=author&query=Joutsenvaara%2C+J">J. Joutsenvaara</a>, <a href="/search/physics?searchtype=author&query=Mengucci%2C+A">A. Mengucci</a>, <a href="/search/physics?searchtype=author&query=Meroni%2C+E">E. Meroni</a>, <a href="/search/physics?searchtype=author&query=Steiger%2C+H">H. Steiger</a>, <a href="/search/physics?searchtype=author&query=Mantovani%2C+F">F. Mantovani</a>, <a href="/search/physics?searchtype=author&query=Ranucci%2C+G">G. Ranucci</a>, <a href="/search/physics?searchtype=author&query=Andronico%2C+G">G. Andronico</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+V">V. Antonelli</a>, <a href="/search/physics?searchtype=author&query=Baldoncini%2C+M">M. Baldoncini</a>, <a href="/search/physics?searchtype=author&query=Bellato%2C+M">M. Bellato</a>, <a href="/search/physics?searchtype=author&query=Bernieri%2C+E">E. Bernieri</a>, <a href="/search/physics?searchtype=author&query=Brugnera%2C+R">R. Brugnera</a>, <a href="/search/physics?searchtype=author&query=Budano%2C+A">A. Budano</a>, <a href="/search/physics?searchtype=author&query=Buscemi%2C+M">M. Buscemi</a>, <a href="/search/physics?searchtype=author&query=Bussino%2C+S">S. Bussino</a>, <a href="/search/physics?searchtype=author&query=Caruso%2C+R">R. Caruso</a> , et al. (46 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1902.05288v1-abstract-short" style="display: inline;"> This paper describes the design, construction principles and operations of the distillation and stripping pilot plants tested at the Daya Bay Neutrino Laboratory, with the perspective to adapt this processes, system cleanliness and leak-tightness to the final full scale plants that will be used for the purification of the liquid scintillator used in the JUNO neutrino detector. The main goal of the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05288v1-abstract-full').style.display = 'inline'; document.getElementById('1902.05288v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.05288v1-abstract-full" style="display: none;"> This paper describes the design, construction principles and operations of the distillation and stripping pilot plants tested at the Daya Bay Neutrino Laboratory, with the perspective to adapt this processes, system cleanliness and leak-tightness to the final full scale plants that will be used for the purification of the liquid scintillator used in the JUNO neutrino detector. The main goal of these plants is to remove radio impurities from the liquid scintillator while increasing its optical attenuation length. Purification of liquid scintillator will be performed with a system combining alumina oxide, distillation, water extraction and steam (or N2 gas) stripping. Such a combined system will aim at obtaining a total attenuation length greater than 20 m at 430 nm, and a bulk radiopurity for 238U and 232Th in the 10-15 to 10-17 g/g range. The pilot plants commissioning and operation have also provided valuable information on the degree of reliability of their main components, which will be particularly useful for the design of the final full scale purification equipment for the JUNO liquid scintillator. This paper describe two of the five pilot plants since the Alumina Column, Fluor mixing and the Water Extraction plants are in charge of the Chinese part of the collaboration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05288v1-abstract-full').style.display = 'none'; document.getElementById('1902.05288v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nuclear Instrumentation and Methods in Physics Research A 925 (2019) 6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.01557">arXiv:1901.01557</a> <span> [<a href="https://arxiv.org/pdf/1901.01557">pdf</a>, <a href="https://arxiv.org/format/1901.01557">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> </div> <p class="title is-5 mathjax"> Spectral Properties of Effective Dynamics from Conditional Expectations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=N%C3%BCske%2C+F">Feliks N眉ske</a>, <a href="/search/physics?searchtype=author&query=Koltai%2C+P">P茅ter Koltai</a>, <a href="/search/physics?searchtype=author&query=Boninsegna%2C+L">Lorenzo Boninsegna</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.01557v3-abstract-short" style="display: inline;"> The reduction of high-dimensional systems to effective models on a smaller set of variables is an essential task in many areas of science. For stochastic dynamics governed by diffusion processes, a general procedure to find effective equations is the conditioning approach. In this paper, we are interested in the spectrum of the generator of the resulting effective dynamics, and how it compares to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.01557v3-abstract-full').style.display = 'inline'; document.getElementById('1901.01557v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.01557v3-abstract-full" style="display: none;"> The reduction of high-dimensional systems to effective models on a smaller set of variables is an essential task in many areas of science. For stochastic dynamics governed by diffusion processes, a general procedure to find effective equations is the conditioning approach. In this paper, we are interested in the spectrum of the generator of the resulting effective dynamics, and how it compares to the spectrum of the full generator. We prove a new relative error bound in terms of the eigenfunction approximation error for reversible systems. We also present numerical examples indicating that if Kramers--Moyal (KM) type approximations are used to compute the spectrum of the reduced generator, it seems largely insensitive to the time window used for the KM estimators. We analyze the implications of these observations for systems driven by underdamped Langevin dynamics, and show how meaningful effective dynamics can be defined in this setting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.01557v3-abstract-full').style.display = 'none'; document.getElementById('1901.01557v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.10722">arXiv:1812.10722</a> <span> [<a href="https://arxiv.org/pdf/1812.10722">pdf</a>, <a href="https://arxiv.org/format/1812.10722">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1103/PhysRevE.100.063305">10.1103/PhysRevE.100.063305 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Computational nanoplasmonics in the quasistatic limit for biosensing applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Clementi%2C+N+C">Natalia C. Clementi</a>, <a href="/search/physics?searchtype=author&query=Cooper%2C+C+D">Christopher D. Cooper</a>, <a href="/search/physics?searchtype=author&query=Barba%2C+L+A">Lorena A. Barba</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1812.10722v4-abstract-short" style="display: inline;"> This work uses the long-wavelength limit to compute LSPR response of biosensors, expanding the open-source PyGBe code to compute the extinction cross-section of metallic nanoparticles in the presence of any target for sensing. The target molecule is represented by a surface mesh, based on its crystal structure. PyGBe is research software for continuum electrostatics, written in Python with computa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.10722v4-abstract-full').style.display = 'inline'; document.getElementById('1812.10722v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.10722v4-abstract-full" style="display: none;"> This work uses the long-wavelength limit to compute LSPR response of biosensors, expanding the open-source PyGBe code to compute the extinction cross-section of metallic nanoparticles in the presence of any target for sensing. The target molecule is represented by a surface mesh, based on its crystal structure. PyGBe is research software for continuum electrostatics, written in Python with computationally expensive parts accelerated on GPU hardware, via PyCUDA. It is also accelerated algorithmically via a treecode that offers O(N log N) computational complexity. These features allow PyGBe to handle problems with half a million boundary elements or more. Using a model problem consisting of an isolated silver nanosphere in an electric field, our results show grid convergence as 1/N, and accurate computation of the extinction cross-section as a function of wavelength (compared with an analytical solution). For a model of a sensor-analyte system, consisting of a spherical silver nanoparticle and a set of bovine serum albumin (BSA) proteins, our results again obtain grid convergence as 1/N (with respect to the Richardson extrapolated value). Computing the LSPR response as a function of wavelength in the presence of BSA proteins captures a red-shift of 0.5 nm in the resonance frequency due to the presence of the analytes at 1-nm distance. The final result is a sensitivity study of the biosensor model, obtaining the shift in resonance frequency for various distances between the proteins and the nanoparticle. All results in this paper are fully reproducible, and we have deposited in archival data repositories all the materials needed to run the computations again and re-create the figures. PyGBe is open source under a permissive license and openly developed. Documentation is available at http://barbagroup.github.io/pygbe/docs/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.10722v4-abstract-full').style.display = 'none'; document.getElementById('1812.10722v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 12 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. E 100, 063305 (2019) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.01736">arXiv:1812.01736</a> <span> [<a href="https://arxiv.org/pdf/1812.01736">pdf</a>, <a href="https://arxiv.org/format/1812.01736">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Machine Learning of coarse-grained Molecular Dynamics Force Fields </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Wang%2C+J">Jiang Wang</a>, <a href="/search/physics?searchtype=author&query=Olsson%2C+S">Simon Olsson</a>, <a href="/search/physics?searchtype=author&query=Wehmeyer%2C+C">Christoph Wehmeyer</a>, <a href="/search/physics?searchtype=author&query=Perez%2C+A">Adria Perez</a>, <a href="/search/physics?searchtype=author&query=Charron%2C+N+E">Nicholas E. Charron</a>, <a href="/search/physics?searchtype=author&query=de+Fabritiis%2C+G">Gianni de Fabritiis</a>, <a href="/search/physics?searchtype=author&query=Noe%2C+F">Frank Noe</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1812.01736v3-abstract-short" style="display: inline;"> Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.01736v3-abstract-full').style.display = 'inline'; document.getElementById('1812.01736v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.01736v3-abstract-full" style="display: none;"> Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multi-body terms that emerge from the dimensionality reduction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.01736v3-abstract-full').style.display = 'none'; document.getElementById('1812.01736v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1701.01665">arXiv:1701.01665</a> <span> [<a href="https://arxiv.org/pdf/1701.01665">pdf</a>, <a href="https://arxiv.org/format/1701.01665">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistical Mechanics">cond-mat.stat-mech</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/1.4976518">10.1063/1.4976518 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=N%C3%BCske%2C+F">Feliks N眉ske</a>, <a href="/search/physics?searchtype=author&query=Wu%2C+H">Hao Wu</a>, <a href="/search/physics?searchtype=author&query=Prinz%2C+J">Jan-Hendrik Prinz</a>, <a href="/search/physics?searchtype=author&query=Wehmeyer%2C+C">Christoph Wehmeyer</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a>, <a href="/search/physics?searchtype=author&query=No%C3%A9%2C+F">Frank No茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1701.01665v1-abstract-short" style="display: inline;"> Many state of the art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data from large numbers of relatively short trajectories are integrated. In this context, Markov state models (MSMs) are extremely popular because they can be used to compute stationary quantities and long-time kinetics from ens… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.01665v1-abstract-full').style.display = 'inline'; document.getElementById('1701.01665v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1701.01665v1-abstract-full" style="display: none;"> Many state of the art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data from large numbers of relatively short trajectories are integrated. In this context, Markov state models (MSMs) are extremely popular because they can be used to compute stationary quantities and long-time kinetics from ensembles of short simulations, provided that these short simulations are in "local equilibrium" within the MSM states. However, in the last over 15 years since the inception of MSMs, it has been controversially discussed and not yet been answered how deviations from local equilibrium can be detected, whether these deviations induce a practical bias in MSM estimation, and how to correct for them. In this paper, we address these issues: We systematically analyze the estimation of Markov state models (MSMs) from short non-equilibrium simulations, and we provide an expression for the error between unbiased transition probabilities and the expected estimate from many short simulations. We show that the unbiased MSM estimate can be obtained even from relatively short non-equilibrium simulations in the limit of long lag times and good discretization. Further, we exploit observable operator model (OOM) theory to derive an unbiased estimator for the MSM transition matrix that corrects for the effect of starting out of equilibrium, even when short lag times are used. Finally, we show how the OOM framework can be used to estimate the exact eigenvalues or relaxation timescales of the system without estimating an MSM transition matrix, which allows us to practically assess the discretization quality of the MSM. Applications to model systems and molecular dynamics simulation data of alanine dipeptide are included for illustration. The improved MSM estimator is implemented in PyEMMA as of version 2.3. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.01665v1-abstract-full').style.display = 'none'; document.getElementById('1701.01665v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1506.06259">arXiv:1506.06259</a> <span> [<a href="https://arxiv.org/pdf/1506.06259">pdf</a>, <a href="https://arxiv.org/format/1506.06259">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> </div> </div> <p class="title is-5 mathjax"> Kinetic distance and kinetic maps from molecular dynamics simulation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Noe%2C+F">Frank Noe</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+C">Cecilia Clementi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1506.06259v1-abstract-short" style="display: inline;"> Characterizing macromolecular kinetics from molecular dynamics (MD) simulations requires a distance metric that can distinguish slowly-interconverting states. Here we build upon diffusion map theory and define a kinetic distance for irreducible Markov processes that quantifies how slowly molecular conformations interconvert. The kinetic distance can be computed given a model that approximates the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.06259v1-abstract-full').style.display = 'inline'; document.getElementById('1506.06259v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1506.06259v1-abstract-full" style="display: none;"> Characterizing macromolecular kinetics from molecular dynamics (MD) simulations requires a distance metric that can distinguish slowly-interconverting states. Here we build upon diffusion map theory and define a kinetic distance for irreducible Markov processes that quantifies how slowly molecular conformations interconvert. The kinetic distance can be computed given a model that approximates the eigenvalues and eigenvectors (reaction coordinates) of the MD Markov operator. Here we employ the time-lagged independent component analysis (TICA). The TICA components can be scaled to provide a kinetic map in which the Euclidean distance corresponds to the kinetic distance. As a result, the question of how many TICA dimensions should be kept in a dimensionality reduction approach becomes obsolete, and one parameter less needs to be specified in the kinetic model construction. We demonstrate the approach using TICA and Markov state model (MSM) analyses for illustrative models, protein conformation dynamics in bovine pancreatic trypsin inhibitor and protein-inhibitor association in trypsin and benzamidine. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1506.06259v1-abstract-full').style.display = 'none'; document.getElementById('1506.06259v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 June, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2015. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1503.08150">arXiv:1503.08150</a> <span> [<a href="https://arxiv.org/pdf/1503.08150">pdf</a>, <a href="https://arxiv.org/format/1503.08150">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/1.4931113">10.1063/1.4931113 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Probing protein orientation near charged nanosurfaces for simulation-assisted biosensor design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Cooper%2C+C+D">Christopher D. Cooper</a>, <a href="/search/physics?searchtype=author&query=Clementi%2C+N+C">Natalia C. Clementi</a>, <a href="/search/physics?searchtype=author&query=Barba%2C+L+A">Lorena A. Barba</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1503.08150v4-abstract-short" style="display: inline;"> Protein-surface interactions are ubiquitous in biological processes and bioengineering, yet are not fully understood. In biosensors, a key factor determining the sensitivity and thus the performance of the device is the orientation of the ligand molecules on the bioactive device surface. Adsorption studies thus seek to determine how orientation can be influenced by surface preparation. In this wor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.08150v4-abstract-full').style.display = 'inline'; document.getElementById('1503.08150v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1503.08150v4-abstract-full" style="display: none;"> Protein-surface interactions are ubiquitous in biological processes and bioengineering, yet are not fully understood. In biosensors, a key factor determining the sensitivity and thus the performance of the device is the orientation of the ligand molecules on the bioactive device surface. Adsorption studies thus seek to determine how orientation can be influenced by surface preparation. In this work, protein orientation near charged nanosurfaces is obtained under electrostatic effects using the Poisson-Boltzmann equation, in an implicit-solvent model. Sampling the free energy for protein GB1D4' at a range of tilt and rotation angles with respect to the charged surface, we calculated the probability of the protein orientations and observed a dipolar behavior. This result is consistent with published experimental studies and combined Monte Carlo and molecular dynamics simulations using this small protein, validating our method. More relevant to biosensor technology, antibodies such as immunoglobulin G are still a formidable challenge to molecular simulation, due to their large size. We obtained the probability distribution of orientations for the iso-type IgG2a at varying surface charge and salt concentration. This iso-type was not found to have a preferred orientation in previous studies, unlike the iso-type IgG1 whose larger dipole moment was assumed to make it easier to control. We find that the preferred orientation of IgG2a can be favorable for biosensing with positive surface charge of 0.05C/m$^{2}$ or higher and 37mM salt concentration. The results also show that local interactions dominate over dipole moment for this protein. Improving immunoassay sensitivity may thus be assisted by numerical studies using our method (and open-source code), guiding changes to fabrication protocols or protein engineering of ligand molecules to obtain more favorable orientations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.08150v4-abstract-full').style.display = 'none'; document.getElementById('1503.08150v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 August, 2015; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 March, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 10 figures -- This version is revised post peer review, and supersedes all previous ones. Note that v3 was reduced considerably from the previous ones, due to the material being split in two papers. Another preprint was submitted (arXiv:1506.03745) with the material that was cut of this paper, corresponding to how the papers were submitted to peer-reviewed journals</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> J. Chem. Phys. 143, 124709 (2015) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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