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<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/2411.09864">arXiv:2411.09864</a> <span> [<a href="https://arxiv.org/pdf/2411.09864">pdf</a>, <a href="https://arxiv.org/format/2411.09864">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="High Energy Physics - Experiment">hep-ex</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"> Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Douglas%2C+D">Daniel Douglas</a>, <a href="/search/physics?searchtype=author&query=Mishra%2C+A">Aashwin Mishra</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Petersen%2C+F">Felix Petersen</a>, <a href="/search/physics?searchtype=author&query=Terao%2C+K">Kazuhiro Terao</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="2411.09864v2-abstract-short" style="display: inline;"> Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. Howev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09864v2-abstract-full').style.display = 'inline'; document.getElementById('2411.09864v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09864v2-abstract-full" style="display: none;"> Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. However, chained models pose an additional challenge for uncertainty quantification, especially when input uncertainties need to be propagated. In such cases, a fully uncertainty-aware chain of models is required, where each step accepts a probability distribution over the input space, and produces a probability distribution over the output space. In this work, we present a case study for adapting a single model within an existing chain, designed for reconstruction within neutrino-Argon interactions, developed for neutrino oscillation experiments such as MicroBooNE, ICARUS, and the future DUNE experiment. We test the performance of an input uncertainty-enabled model against an uncertainty-blinded model using a method for generating synthetic noise. By comparing these two, we assess the increase in inference quality achieved by exposing models to upstream uncertainty estimates. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09864v2-abstract-full').style.display = 'none'; document.getElementById('2411.09864v2-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.12881">arXiv:2406.12881</a> <span> [<a href="https://arxiv.org/pdf/2406.12881">pdf</a>, <a href="https://arxiv.org/format/2406.12881">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Towards Unlocking Insights from Logbooks Using AI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Sulc%2C+A">Antonin Sulc</a>, <a href="/search/physics?searchtype=author&query=Bien%2C+A">Alex Bien</a>, <a href="/search/physics?searchtype=author&query=Eichler%2C+A">Annika Eichler</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Rehm%2C+F">Florian Rehm</a>, <a href="/search/physics?searchtype=author&query=Mayet%2C+F">Frank Mayet</a>, <a href="/search/physics?searchtype=author&query=Hartmann%2C+G">Gregor Hartmann</a>, <a href="/search/physics?searchtype=author&query=Hoschouer%2C+H">Hayden Hoschouer</a>, <a href="/search/physics?searchtype=author&query=Tuennermann%2C+H">Henrik Tuennermann</a>, <a href="/search/physics?searchtype=author&query=Kaiser%2C+J">Jan Kaiser</a>, <a href="/search/physics?searchtype=author&query=John%2C+J+S">Jason St. John</a>, <a href="/search/physics?searchtype=author&query=Maldonado%2C+J">Jennefer Maldonado</a>, <a href="/search/physics?searchtype=author&query=Hazelwood%2C+K">Kyle Hazelwood</a>, <a href="/search/physics?searchtype=author&query=Kammering%2C+R">Raimund Kammering</a>, <a href="/search/physics?searchtype=author&query=Hellert%2C+T">Thorsten Hellert</a>, <a href="/search/physics?searchtype=author&query=Wilksen%2C+T">Tim Wilksen</a>, <a href="/search/physics?searchtype=author&query=Kain%2C+V">Verena Kain</a>, <a href="/search/physics?searchtype=author&query=Hu%2C+W">Wan-Lin Hu</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="2406.12881v1-abstract-short" style="display: inline;"> Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12881v1-abstract-full').style.display = 'inline'; document.getElementById('2406.12881v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.12881v1-abstract-full" style="display: none;"> Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12881v1-abstract-full').style.display = 'none'; document.getElementById('2406.12881v1-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 May, 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">5 pages, 1 figure, 15th International Particle Accelerator Conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.03225">arXiv:2403.03225</a> <span> [<a href="https://arxiv.org/pdf/2403.03225">pdf</a>, <a href="https://arxiv.org/format/2403.03225">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> </div> </div> <p class="title is-5 mathjax"> More Sample-Efficient Tuning of Particle Accelerators with Bayesian Optimization and Prior Mean Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Boltz%2C+T">Tobias Boltz</a>, <a href="/search/physics?searchtype=author&query=Martinez%2C+J+L">Jose L. Martinez</a>, <a href="/search/physics?searchtype=author&query=Xu%2C+C">Connie Xu</a>, <a href="/search/physics?searchtype=author&query=Baker%2C+K+R+L">Kathryn R. L. Baker</a>, <a href="/search/physics?searchtype=author&query=Roussel%2C+R">Ryan Roussel</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Mustapha%2C+B">Brahim Mustapha</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A+L">Auralee L. Edelen</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="2403.03225v2-abstract-short" style="display: inline;"> Tuning particle accelerators is a challenging and time-consuming task, but can be automated and carried out efficiently through the use of suitable optimization algorithms. With successful applications at various facilities, Bayesian optimization using Gaussian process modeling has proven to be a particularly powerful tool to address these challenges in practice. One of its major benefits is that… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03225v2-abstract-full').style.display = 'inline'; document.getElementById('2403.03225v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.03225v2-abstract-full" style="display: none;"> Tuning particle accelerators is a challenging and time-consuming task, but can be automated and carried out efficiently through the use of suitable optimization algorithms. With successful applications at various facilities, Bayesian optimization using Gaussian process modeling has proven to be a particularly powerful tool to address these challenges in practice. One of its major benefits is that it allows incorporating prior information, such as knowledge about the shape of the objective function or predictions based on archived data, simulations or surrogate models, into the model. In this work, we propose the use of a neural network model as an efficient way to include prior knowledge about the objective function into the Bayesian optimization process to speed up convergence. We report results obtained in simulations and experiments using neural network priors to perform optimization of electron and heavy-ion accelerator facilities, specifically the Linac Coherent Light Source and the Argonne Tandem Linear Accelerator System. Finally, we evaluate how the accuracy of the prior mean predictions affect optimization performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03225v2-abstract-full').style.display = 'none'; document.getElementById('2403.03225v2-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.16078">arXiv:2312.16078</a> <span> [<a href="https://arxiv.org/pdf/2312.16078">pdf</a>, <a href="https://arxiv.org/format/2312.16078">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="Data Analysis, Statistics and Probability">physics.data-an</span> </div> </div> <p class="title is-5 mathjax"> Targeted materials discovery using Bayesian algorithm execution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Chitturi%2C+S">Sathya Chitturi</a>, <a href="/search/physics?searchtype=author&query=Ramdas%2C+A">Akash Ramdas</a>, <a href="/search/physics?searchtype=author&query=Wu%2C+Y">Yue Wu</a>, <a href="/search/physics?searchtype=author&query=Rohr%2C+B">Brian Rohr</a>, <a href="/search/physics?searchtype=author&query=Ermon%2C+S">Stefano Ermon</a>, <a href="/search/physics?searchtype=author&query=Dionne%2C+J">Jennifer Dionne</a>, <a href="/search/physics?searchtype=author&query=da+Jornada%2C+F+H">Felipe H. da Jornada</a>, <a href="/search/physics?searchtype=author&query=Dunne%2C+M">Mike Dunne</a>, <a href="/search/physics?searchtype=author&query=Tassone%2C+C">Christopher Tassone</a>, <a href="/search/physics?searchtype=author&query=Neiswanger%2C+W">Willie Neiswanger</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</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="2312.16078v1-abstract-short" style="display: inline;"> Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.16078v1-abstract-full').style.display = 'inline'; document.getElementById('2312.16078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.16078v1-abstract-full" style="display: none;"> Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data acquisition strategies (SwitchBAX, InfoBAX, and MeanBAX). Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We evaluate this approach on datasets for TiO$_2$ nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.16078v1-abstract-full').style.display = 'none'; document.getElementById('2312.16078v1-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">29 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/2312.05667">arXiv:2312.05667</a> <span> [<a href="https://arxiv.org/pdf/2312.05667">pdf</a>, <a href="https://arxiv.org/format/2312.05667">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> </div> </div> <p class="title is-5 mathjax"> Bayesian Optimization Algorithms for Accelerator Physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Roussel%2C+R">Ryan Roussel</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A+L">Auralee L. Edelen</a>, <a href="/search/physics?searchtype=author&query=Boltz%2C+T">Tobias Boltz</a>, <a href="/search/physics?searchtype=author&query=Kennedy%2C+D">Dylan Kennedy</a>, <a href="/search/physics?searchtype=author&query=Zhang%2C+Z">Zhe Zhang</a>, <a href="/search/physics?searchtype=author&query=Ji%2C+F">Fuhao Ji</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+X">Xiaobiao Huang</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Garcia%2C+A+S">Andrea Santamaria Garcia</a>, <a href="/search/physics?searchtype=author&query=Xu%2C+C">Chenran Xu</a>, <a href="/search/physics?searchtype=author&query=Kaiser%2C+J">Jan Kaiser</a>, <a href="/search/physics?searchtype=author&query=Pousa%2C+A+F">Angel Ferran Pousa</a>, <a href="/search/physics?searchtype=author&query=Eichler%2C+A">Annika Eichler</a>, <a href="/search/physics?searchtype=author&query=Lubsen%2C+J+O">Jannis O. Lubsen</a>, <a href="/search/physics?searchtype=author&query=Isenberg%2C+N+M">Natalie M. Isenberg</a>, <a href="/search/physics?searchtype=author&query=Gao%2C+Y">Yuan Gao</a>, <a href="/search/physics?searchtype=author&query=Kuklev%2C+N">Nikita Kuklev</a>, <a href="/search/physics?searchtype=author&query=Martinez%2C+J">Jose Martinez</a>, <a href="/search/physics?searchtype=author&query=Mustapha%2C+B">Brahim Mustapha</a>, <a href="/search/physics?searchtype=author&query=Kain%2C+V">Verena Kain</a>, <a href="/search/physics?searchtype=author&query=Lin%2C+W">Weijian Lin</a>, <a href="/search/physics?searchtype=author&query=Liuzzo%2C+S+M">Simone Maria Liuzzo</a>, <a href="/search/physics?searchtype=author&query=John%2C+J+S">Jason St. John</a>, <a href="/search/physics?searchtype=author&query=Streeter%2C+M+J+V">Matthew J. V. Streeter</a>, <a href="/search/physics?searchtype=author&query=Lehe%2C+R">Remi Lehe</a> , et al. (1 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="2312.05667v2-abstract-short" style="display: inline;"> Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.05667v2-abstract-full').style.display = 'inline'; document.getElementById('2312.05667v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.05667v2-abstract-full" style="display: none;"> Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.05667v2-abstract-full').style.display = 'none'; document.getElementById('2312.05667v2-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 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.04639">arXiv:2309.04639</a> <span> [<a href="https://arxiv.org/pdf/2309.04639">pdf</a>, <a href="https://arxiv.org/format/2309.04639">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"> Differentiable Simulation of a Liquid Argon Time Projection Chamber </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Gasiorowski%2C+S">Sean Gasiorowski</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Y">Yifan Chen</a>, <a href="/search/physics?searchtype=author&query=Nashed%2C+Y">Youssef Nashed</a>, <a href="/search/physics?searchtype=author&query=Granger%2C+P">Pierre Granger</a>, <a href="/search/physics?searchtype=author&query=Mironov%2C+C">Camelia Mironov</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Terao%2C+K">Kazuhiro Terao</a>, <a href="/search/physics?searchtype=author&query=Tsang%2C+K+V">Ka Vang Tsang</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="2309.04639v1-abstract-short" style="display: inline;"> Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The fidelity of these simulators relative to real, measured data is limited by the modeling of the p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.04639v1-abstract-full').style.display = 'inline'; document.getElementById('2309.04639v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.04639v1-abstract-full" style="display: none;"> Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The fidelity of these simulators relative to real, measured data is limited by the modeling of the physical detectors used for data collection. This modeling can be improved by performing dedicated calibration measurements. Conventional approaches calibrate individual detector parameters or processes one at a time. However, the impact of detector processes is entangled, making this a poor description of the underlying physics. We introduce a differentiable simulator that enables a gradient-based optimization, allowing for the first time a simultaneous calibration of all detector parameters. We describe the procedure of making a differentiable simulator, highlighting the challenges of retaining the physics quality of the standard, non-differentiable version while providing meaningful gradient information. We further discuss the advantages and drawbacks of using our differentiable simulator for calibration. Finally, we provide a starting point for extensions to our approach, including applications of the differentiable simulator to physics analysis pipelines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.04639v1-abstract-full').style.display = 'none'; document.getElementById('2309.04639v1-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> 8 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.02333">arXiv:2309.02333</a> <span> [<a href="https://arxiv.org/pdf/2309.02333">pdf</a>, <a href="https://arxiv.org/format/2309.02333">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Humble%2C+R">Ryan Humble</a>, <a href="/search/physics?searchtype=author&query=Colocho%2C+W">William Colocho</a>, <a href="/search/physics?searchtype=author&query=O%27Shea%2C+F">Finn O'Shea</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Darve%2C+E">Eric Darve</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="2309.02333v1-abstract-short" style="display: inline;"> Significant advances in utilizing deep learning for anomaly detection have been made in recent years. However, these methods largely assume the existence of a normal training set (i.e., uncontaminated by anomalies) or even a completely labeled training set. In many complex engineering systems, such as particle accelerators, labels are sparse and expensive; in order to perform anomaly detection in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02333v1-abstract-full').style.display = 'inline'; document.getElementById('2309.02333v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.02333v1-abstract-full" style="display: none;"> Significant advances in utilizing deep learning for anomaly detection have been made in recent years. However, these methods largely assume the existence of a normal training set (i.e., uncontaminated by anomalies) or even a completely labeled training set. In many complex engineering systems, such as particle accelerators, labels are sparse and expensive; in order to perform anomaly detection in these cases, we must drop these assumptions and utilize a completely unsupervised method. This paper introduces the Resilient Variational Autoencoder (ResVAE), a deep generative model specifically designed for anomaly detection. ResVAE exhibits resilience to anomalies present in the training data and provides feature-level anomaly attribution. During the training process, ResVAE learns the anomaly probability for each sample as well as each individual feature, utilizing these probabilities to effectively disregard anomalous examples in the training data. We apply our proposed method to detect anomalies in the accelerator status at the SLAC Linac Coherent Light Source (LCLS). By utilizing shot-to-shot data from the beam position monitoring system, we demonstrate the exceptional capability of ResVAE in identifying various types of anomalies that are visible in the accelerator. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02333v1-abstract-full').style.display = 'none'; document.getElementById('2309.02333v1-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 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.03949">arXiv:2304.03949</a> <span> [<a href="https://arxiv.org/pdf/2304.03949">pdf</a>, <a href="https://arxiv.org/format/2304.03949">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</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/10.1038/s41467-023-41378-4">10.1038/s41467-023-41378-4 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Capturing dynamical correlations using implicit neural representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Chitturi%2C+S">Sathya Chitturi</a>, <a href="/search/physics?searchtype=author&query=Ji%2C+Z">Zhurun Ji</a>, <a href="/search/physics?searchtype=author&query=Petsch%2C+A">Alexander Petsch</a>, <a href="/search/physics?searchtype=author&query=Peng%2C+C">Cheng Peng</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Z">Zhantao Chen</a>, <a href="/search/physics?searchtype=author&query=Plumley%2C+R">Rajan Plumley</a>, <a href="/search/physics?searchtype=author&query=Dunne%2C+M">Mike Dunne</a>, <a href="/search/physics?searchtype=author&query=Mardanya%2C+S">Sougata Mardanya</a>, <a href="/search/physics?searchtype=author&query=Chowdhury%2C+S">Sugata Chowdhury</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+H">Hongwei Chen</a>, <a href="/search/physics?searchtype=author&query=Bansil%2C+A">Arun Bansil</a>, <a href="/search/physics?searchtype=author&query=Feiguin%2C+A">Adrian Feiguin</a>, <a href="/search/physics?searchtype=author&query=Kolesnikov%2C+A">Alexander Kolesnikov</a>, <a href="/search/physics?searchtype=author&query=Prabhakaran%2C+D">Dharmalingam Prabhakaran</a>, <a href="/search/physics?searchtype=author&query=Hayden%2C+S">Stephen Hayden</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Jia%2C+C">Chunjing Jia</a>, <a href="/search/physics?searchtype=author&query=Nashed%2C+Y">Youssef Nashed</a>, <a href="/search/physics?searchtype=author&query=Turner%2C+J">Joshua Turner</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="2304.03949v1-abstract-short" style="display: inline;"> The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor, S(Q, $蠅$), with inelastic neutron or x-ray scattering techniques and comparing this against a calculated dynamical model. Here, we develop an artific… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.03949v1-abstract-full').style.display = 'inline'; document.getElementById('2304.03949v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.03949v1-abstract-full" style="display: none;"> The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor, S(Q, $蠅$), with inelastic neutron or x-ray scattering techniques and comparing this against a calculated dynamical model. Here, we develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. We benchmark this approach on a Linear Spin Wave Theory (LSWT) simulator and advanced inelastic neutron scattering data from the square-lattice spin-1 antiferromagnet La$_2$NiO$_4$. We find that the model predicts the unknown parameters with excellent agreement relative to analytical fitting. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data, without the need for human-guided peak finding and fitting algorithms. This prototypical approach promises a new technology for this field to automatically detect and refine more advanced models for ordered quantum systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.03949v1-abstract-full').style.display = 'none'; document.getElementById('2304.03949v1-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> 8 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">12 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/2211.01505">arXiv:2211.01505</a> <span> [<a href="https://arxiv.org/pdf/2211.01505">pdf</a>, <a href="https://arxiv.org/format/2211.01505">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Lei%2C+M">Minjie Lei</a>, <a href="/search/physics?searchtype=author&query=Tsang%2C+K+V">Ka Vang Tsang</a>, <a href="/search/physics?searchtype=author&query=Gasiorowski%2C+S">Sean Gasiorowski</a>, <a href="/search/physics?searchtype=author&query=Li%2C+C">Chuan Li</a>, <a href="/search/physics?searchtype=author&query=Nashed%2C+Y">Youssef Nashed</a>, <a href="/search/physics?searchtype=author&query=Petrillo%2C+G">Gianluca Petrillo</a>, <a href="/search/physics?searchtype=author&query=Piazza%2C+O">Olivia Piazza</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Terao%2C+K">Kazuhiro Terao</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="2211.01505v1-abstract-short" style="display: inline;"> Optical photons are used as signal in a wide variety of particle detectors. Modern neutrino experiments employ hundreds to tens of thousands of photon detectors to observe signal from millions to billions of scintillation photons produced from energy deposition of charged particles. These neutrino detectors are typically large, containing kilotons of target volume, with different optical propertie… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.01505v1-abstract-full').style.display = 'inline'; document.getElementById('2211.01505v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.01505v1-abstract-full" style="display: none;"> Optical photons are used as signal in a wide variety of particle detectors. Modern neutrino experiments employ hundreds to tens of thousands of photon detectors to observe signal from millions to billions of scintillation photons produced from energy deposition of charged particles. These neutrino detectors are typically large, containing kilotons of target volume, with different optical properties. Modeling individual photon propagation in form of look-up table requires huge computational resources. As the size of a table increases with detector volume for a fixed resolution, this method scales poorly for future larger detectors. Alternative approaches such as fitting a polynomial to the model could address the memory issue, but results in poorer performance. Both look-up table and fitting approaches are prone to discrepancies between the detector simulation and the data collected. We propose a new approach using SIREN, an implicit neural representation with periodic activation functions, to model the look-up table as a 3D scene and reproduces the acceptance map with high accuracy. The number of parameters in our SIREN model is orders of magnitude smaller than the number of voxels in the look-up table. As it models an underlying functional shape, SIREN is scalable to a larger detector. Furthermore, SIREN can successfully learn the spatial gradients of the photon library, providing additional information for downstream applications. Finally, as SIREN is a neural network representation, it is differentiable with respect to its parameters, and therefore tunable via gradient descent. We demonstrate the potential of optimizing SIREN directly on real data, which mitigates the concern of data vs. simulation discrepancies. We further present an application for data reconstruction where SIREN is used to form a likelihood function for photon statistics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.01505v1-abstract-full').style.display = 'none'; document.getElementById('2211.01505v1-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> 2 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.10137">arXiv:2210.10137</a> <span> [<a href="https://arxiv.org/pdf/2210.10137">pdf</a>, <a href="https://arxiv.org/format/2210.10137">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> </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/10.1109/XLOOP56614.2022.00006">10.1109/XLOOP56614.2022.00006 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Chen%2C+H">Hongwei Chen</a>, <a href="/search/physics?searchtype=author&query=Chitturi%2C+S+R">Sathya R. Chitturi</a>, <a href="/search/physics?searchtype=author&query=Plumley%2C+R">Rajan Plumley</a>, <a href="/search/physics?searchtype=author&query=Shen%2C+L">Lingjia Shen</a>, <a href="/search/physics?searchtype=author&query=Drucker%2C+N+C">Nathan C. Drucker</a>, <a href="/search/physics?searchtype=author&query=Burdet%2C+N">Nicolas Burdet</a>, <a href="/search/physics?searchtype=author&query=Peng%2C+C">Cheng Peng</a>, <a href="/search/physics?searchtype=author&query=Mardanya%2C+S">Sougata Mardanya</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Mishra%2C+A">Aashwin Mishra</a>, <a href="/search/physics?searchtype=author&query=Yoon%2C+C+H">Chun Hong Yoon</a>, <a href="/search/physics?searchtype=author&query=Song%2C+S">Sanghoon Song</a>, <a href="/search/physics?searchtype=author&query=Chollet%2C+M">Matthieu Chollet</a>, <a href="/search/physics?searchtype=author&query=Fabbris%2C+G">Gilberto Fabbris</a>, <a href="/search/physics?searchtype=author&query=Dunne%2C+M">Mike Dunne</a>, <a href="/search/physics?searchtype=author&query=Nelson%2C+S">Silke Nelson</a>, <a href="/search/physics?searchtype=author&query=Li%2C+M">Mingda Li</a>, <a href="/search/physics?searchtype=author&query=Lindenberg%2C+A">Aaron Lindenberg</a>, <a href="/search/physics?searchtype=author&query=Jia%2C+C">Chunjing Jia</a>, <a href="/search/physics?searchtype=author&query=Nashed%2C+Y">Youssef Nashed</a>, <a href="/search/physics?searchtype=author&query=Bansil%2C+A">Arun Bansil</a>, <a href="/search/physics?searchtype=author&query=Chowdhury%2C+S">Sugata Chowdhury</a>, <a href="/search/physics?searchtype=author&query=Feiguin%2C+A+E">Adrian E. Feiguin</a>, <a href="/search/physics?searchtype=author&query=Turner%2C+J+J">Joshua J. Turner</a>, <a href="/search/physics?searchtype=author&query=Thayer%2C+J+B">Jana B. Thayer</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="2210.10137v1-abstract-short" style="display: inline;"> The advent of next-generation X-ray free electron lasers will be capable of delivering X-rays at a repetition rate approaching 1 MHz continuously. This will require the development of data systems to handle experiments at these type of facilities, especially for high throughput applications, such as femtosecond X-ray crystallography and X-ray photon fluctuation spectroscopy. Here, we demonstrate a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.10137v1-abstract-full').style.display = 'inline'; document.getElementById('2210.10137v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.10137v1-abstract-full" style="display: none;"> The advent of next-generation X-ray free electron lasers will be capable of delivering X-rays at a repetition rate approaching 1 MHz continuously. This will require the development of data systems to handle experiments at these type of facilities, especially for high throughput applications, such as femtosecond X-ray crystallography and X-ray photon fluctuation spectroscopy. Here, we demonstrate a framework which captures single shot X-ray data at the LCLS and implements a machine-learning algorithm to automatically extract the contrast parameter from the collected data. We measure the time required to return the results and assess the feasibility of using this framework at high data volume. We use this experiment to determine the feasibility of solutions for `live' data analysis at the MHz repetition rate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.10137v1-abstract-full').style.display = 'none'; document.getElementById('2210.10137v1-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 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2022 4th Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP) (2022) 1-9 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.15121">arXiv:2209.15121</a> <span> [<a href="https://arxiv.org/pdf/2209.15121">pdf</a>, <a href="https://arxiv.org/format/2209.15121">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="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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"> Heterogeneous reconstruction of deformable atomic models in Cryo-EM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Nashed%2C+Y">Youssef Nashed</a>, <a href="/search/physics?searchtype=author&query=Peck%2C+A">Ariana Peck</a>, <a href="/search/physics?searchtype=author&query=Martel%2C+J">Julien Martel</a>, <a href="/search/physics?searchtype=author&query=Levy%2C+A">Axel Levy</a>, <a href="/search/physics?searchtype=author&query=Koo%2C+B">Bongjin Koo</a>, <a href="/search/physics?searchtype=author&query=Wetzstein%2C+G">Gordon Wetzstein</a>, <a href="/search/physics?searchtype=author&query=Miolane%2C+N">Nina Miolane</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Poitevin%2C+F">Fr茅d茅ric Poitevin</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="2209.15121v1-abstract-short" style="display: inline;"> Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules. Being able to explain this heterogeneity with atomic models would help our understanding of their functional mechanisms but the size and ruggedness of the structural space (the space of atomic 3D cartesian coordinates) presents an immense challenge. Here, we describe a heter… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.15121v1-abstract-full').style.display = 'inline'; document.getElementById('2209.15121v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.15121v1-abstract-full" style="display: none;"> Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules. Being able to explain this heterogeneity with atomic models would help our understanding of their functional mechanisms but the size and ruggedness of the structural space (the space of atomic 3D cartesian coordinates) presents an immense challenge. Here, we describe a heterogeneous reconstruction method based on an atomistic representation whose deformation is reduced to a handful of collective motions through normal mode analysis. Our implementation uses an autoencoder. The encoder jointly estimates the amplitude of motion along the normal modes and the 2D shift between the center of the image and the center of the molecule . The physics-based decoder aggregates a representation of the heterogeneity readily interpretable at the atomic level. We illustrate our method on 3 synthetic datasets corresponding to different distributions along a simulated trajectory of adenylate kinase transitioning from its open to its closed structures. We show for each distribution that our approach is able to recapitulate the intermediate atomic models with atomic-level accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.15121v1-abstract-full').style.display = 'none'; document.getElementById('2209.15121v1-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> 29 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">8 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.04587">arXiv:2209.04587</a> <span> [<a href="https://arxiv.org/pdf/2209.04587">pdf</a>, <a href="https://arxiv.org/format/2209.04587">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/10.1088/2632-2153/ad169f">10.1088/2632-2153/ad169f <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </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/10.1088/2632-2153/ad169f">10.1088/2632-2153/ad169f <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </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/10.1088/2632-2153/ad169f">10.1088/2632-2153/ad169f <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </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/10.1088/2632-2153/ad169f">10.1088/2632-2153/ad169f <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Miskovich%2C+S+A">Sara A. Miskovich</a>, <a href="/search/physics?searchtype=author&query=Neiswanger%2C+W">Willie Neiswanger</a>, <a href="/search/physics?searchtype=author&query=Colocho%2C+W">William Colocho</a>, <a href="/search/physics?searchtype=author&query=Emma%2C+C">Claudio Emma</a>, <a href="/search/physics?searchtype=author&query=Garrahan%2C+J">Jacqueline Garrahan</a>, <a href="/search/physics?searchtype=author&query=Maxwell%2C+T">Timothy Maxwell</a>, <a href="/search/physics?searchtype=author&query=Mayes%2C+C">Christopher Mayes</a>, <a href="/search/physics?searchtype=author&query=Ermon%2C+S">Stefano Ermon</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A">Auralee Edelen</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</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="2209.04587v5-abstract-short" style="display: inline;"> Although beam emittance is critical for the performance of high-brightness accelerators, optimization is often time limited as emittance calculations, commonly done via quadrupole scans, are typically slow. Such calculations are a type of $\textit{multipoint query}$, i.e. each query requires multiple secondary measurements. Traditional black-box optimizers such as Bayesian optimization are slow an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.04587v5-abstract-full').style.display = 'inline'; document.getElementById('2209.04587v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.04587v5-abstract-full" style="display: none;"> Although beam emittance is critical for the performance of high-brightness accelerators, optimization is often time limited as emittance calculations, commonly done via quadrupole scans, are typically slow. Such calculations are a type of $\textit{multipoint query}$, i.e. each query requires multiple secondary measurements. Traditional black-box optimizers such as Bayesian optimization are slow and inefficient when dealing with such objectives as they must acquire the full series of measurements, but return only the emittance, with each query. We propose a new information-theoretic algorithm, Multipoint-BAX, for black-box optimization on multipoint queries, which queries and models individual beam-size measurements using techniques from Bayesian Algorithm Execution (BAX). Our method avoids the slow multipoint query on the accelerator by acquiring points through a $\textit{virtual objective}$, i.e. calculating the emittance objective from a fast learned model rather than directly from the accelerator. We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II). In simulation, our method is 20$\times$ faster and more robust to noise compared to existing methods. In live tests, it matched the hand-tuned emittance at FACET-II and achieved a 24% lower emittance than hand-tuning at LCLS. Our method represents a conceptual shift for optimizing multipoint queries, and we anticipate that it can be readily adapted to similar problems in particle accelerators and other scientific instruments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.04587v5-abstract-full').style.display = 'none'; document.getElementById('2209.04587v5-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Machine Learning: Science and Technology, Dec. 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.04505">arXiv:2209.04505</a> <span> [<a href="https://arxiv.org/pdf/2209.04505">pdf</a>, <a href="https://arxiv.org/format/2209.04505">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-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/PhysRevLett.130.145001">10.1103/PhysRevLett.130.145001 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Roussel%2C+R">Ryan Roussel</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A">Auralee Edelen</a>, <a href="/search/physics?searchtype=author&query=Mayes%2C+C">Christopher Mayes</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Gonzalez-Aguilera%2C+J+P">Juan Pablo Gonzalez-Aguilera</a>, <a href="/search/physics?searchtype=author&query=Kim%2C+S">Seongyeol Kim</a>, <a href="/search/physics?searchtype=author&query=Wisniewski%2C+E">Eric Wisniewski</a>, <a href="/search/physics?searchtype=author&query=Power%2C+J">John Power</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="2209.04505v2-abstract-short" style="display: inline;"> Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional ($>$ 2D) beam properties. In this Letter, we introduce a general-purpose algorithm that combine… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.04505v2-abstract-full').style.display = 'inline'; document.getElementById('2209.04505v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.04505v2-abstract-full" style="display: none;"> Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional ($>$ 2D) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. We demonstrate that our algorithm accurately reconstructs detailed 4D phase space distributions with corresponding confidence intervals in both simulation and experiment using a single focusing quadrupole and diagnostic screen. This technique allows for the measurement of multiple correlated phase spaces simultaneously, which will enable simplified 6D phase space distribution reconstructions in the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.04505v2-abstract-full').style.display = 'none'; document.getElementById('2209.04505v2-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.09064">arXiv:2206.09064</a> <span> [<a href="https://arxiv.org/pdf/2206.09064">pdf</a>, <a href="https://arxiv.org/format/2206.09064">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="Optics">physics.optics</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/10.1063/4.0000161">10.1063/4.0000161 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A machine learning photon detection algorithm for coherent X-ray ultrafast fluctuation analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Chitturi%2C+S+R">Sathya R. Chitturi</a>, <a href="/search/physics?searchtype=author&query=Burdet%2C+N+G">Nicolas G. Burdet</a>, <a href="/search/physics?searchtype=author&query=Nashed%2C+Y">Youssef Nashed</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Mishra%2C+A">Aashwin Mishra</a>, <a href="/search/physics?searchtype=author&query=Lane%2C+T">TJ Lane</a>, <a href="/search/physics?searchtype=author&query=Seaberg%2C+M">Matthew Seaberg</a>, <a href="/search/physics?searchtype=author&query=Esposito%2C+V">Vincent Esposito</a>, <a href="/search/physics?searchtype=author&query=Yoon%2C+C+H">Chun H. Yoon</a>, <a href="/search/physics?searchtype=author&query=Dunne%2C+M">Mike Dunne</a>, <a href="/search/physics?searchtype=author&query=Turner%2C+J+J">Joshua J. Turner</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="2206.09064v1-abstract-short" style="display: inline;"> X-ray free electron laser (XFEL) experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body qu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.09064v1-abstract-full').style.display = 'inline'; document.getElementById('2206.09064v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.09064v1-abstract-full" style="display: none;"> X-ray free electron laser (XFEL) experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body quantum systems at the level of the ultrafast pulse durations, but this method has been limited to a select number of examples and required complex and advanced analytical tools. By applying a new methodology to this problem, we have made qualitative advances in three separate areas that will likely also find application to new fields. As compared to the `droplet-type' models which typically are used to estimate the photon distributions on pixelated detectors to obtain the coherent X-ray speckle patterns, our algorithm pipeline achieves an order of magnitude speedup on CPU hardware and two orders of magnitude improvement on GPU hardware. We also find that it retains accuracy in low-contrast conditions, which is the typical regime for many experiments in structural dynamics. Finally, it can predict photon distributions in high average-intensity applications, a regime which up until now, has not been accessible. Our AI-assisted algorithm will enable a wider adoption of x-ray coherence spectroscopies, by both automating previously challenging analyses and enabling new experiments that were not otherwise feasible without the developments described in this work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.09064v1-abstract-full').style.display = 'none'; document.getElementById('2206.09064v1-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 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">25 pages, 10 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/2206.04626">arXiv:2206.04626</a> <span> [<a href="https://arxiv.org/pdf/2206.04626">pdf</a>, <a href="https://arxiv.org/format/2206.04626">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-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/PhysRevAccelBeams.25.122804">10.1103/PhysRevAccelBeams.25.122804 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Beam-based RF Station Fault Identification at the SLAC Linac Coherent Light Source </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Humble%2C+R">Ryan Humble</a>, <a href="/search/physics?searchtype=author&query=O%27Shea%2C+F+H">Finn H. O'Shea</a>, <a href="/search/physics?searchtype=author&query=Colocho%2C+W">William Colocho</a>, <a href="/search/physics?searchtype=author&query=Gibbs%2C+M">Matt Gibbs</a>, <a href="/search/physics?searchtype=author&query=Chaffee%2C+H">Helen Chaffee</a>, <a href="/search/physics?searchtype=author&query=Darve%2C+E">Eric Darve</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</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="2206.04626v2-abstract-short" style="display: inline;"> Accelerators produce too many signals for a small operations team to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with years of experience. As a result, changes in accelerator performance can require time-intensive consultations with experts to identify the underlying problem. Herein, we focus on a particular anomaly detection task for r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04626v2-abstract-full').style.display = 'inline'; document.getElementById('2206.04626v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.04626v2-abstract-full" style="display: none;"> Accelerators produce too many signals for a small operations team to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with years of experience. As a result, changes in accelerator performance can require time-intensive consultations with experts to identify the underlying problem. Herein, we focus on a particular anomaly detection task for radio-frequency (RF) stations at the Linac Coherent Light Source (LCLS). The existing RF station diagnostics are bandwidth limited, resulting in slow, unreliable signals. As a result, anomaly detection is currently a manual process. We propose a beam-based method, identifying changes in the accelerator status using shot-to-shot data from the beam position monitoring system; by comparing the beam-based anomalies to data from RF stations, we identify the source of the change. We find that our proposed method can be fully automated while identifying more events with fewer false positives than the RF station diagnostics alone. Our automated fault identification system has been used to create a new data set for investigating the interaction between the RF stations and accelerator performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04626v2-abstract-full').style.display = 'none'; document.getElementById('2206.04626v2-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> 2 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.07747">arXiv:2202.07747</a> <span> [<a href="https://arxiv.org/pdf/2202.07747">pdf</a>, <a href="https://arxiv.org/format/2202.07747">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-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/PhysRevLett.128.204801">10.1103/PhysRevLett.128.204801 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Differentiable Preisach Modeling for Characterization and Optimization of Accelerator Systems with Hysteresis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Roussel%2C+R">R. Roussel</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A">A. Edelen</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">D. Ratner</a>, <a href="/search/physics?searchtype=author&query=Dubey%2C+K">K. Dubey</a>, <a href="/search/physics?searchtype=author&query=Gonzalez-Aguilera%2C+J+P">J. P. Gonzalez-Aguilera</a>, <a href="/search/physics?searchtype=author&query=Kim%2C+Y+K">Y. K. Kim</a>, <a href="/search/physics?searchtype=author&query=Kuklev%2C+N">N. Kuklev</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="2202.07747v1-abstract-short" style="display: inline;"> Future improvements in particle accelerator performance is predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high precisi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.07747v1-abstract-full').style.display = 'inline'; document.getElementById('2202.07747v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.07747v1-abstract-full" style="display: none;"> Future improvements in particle accelerator performance is predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high precision accelerators. In this work, we combine the classical Preisach model of hysteresis with machine learning techniques to efficiently create non-parametric, high-fidelity models of arbitrary systems exhibiting hysteresis. We demonstrate that our technique accurately predicts hysteresis effects in physical accelerator magnets. We also experimentally demonstrate how these methods can be used in-situ, where the hysteresis model is combined with a Bayesian statistical model of the beam response, allowing characterization of hysteresis in accelerator magnets solely from measurements of the beam. Furthermore, we explore how using these joint hysteresis-beam models allows us to overcome optimization performance limitations when hysteresis effects are ignored. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.07747v1-abstract-full').style.display = 'none'; document.getElementById('2202.07747v1-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 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.03566">arXiv:2009.03566</a> <span> [<a href="https://arxiv.org/pdf/2009.03566">pdf</a>, <a href="https://arxiv.org/format/2009.03566">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="Accelerator Physics">physics.acc-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/PhysRevAccelBeams.24.072802">10.1103/PhysRevAccelBeams.24.072802 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Physics-informed Gaussian Process for Online Optimization of Particle Accelerators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Hanuka%2C+A">Adi Hanuka</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+X">X. Huang</a>, <a href="/search/physics?searchtype=author&query=Shtalenkova%2C+J">J. Shtalenkova</a>, <a href="/search/physics?searchtype=author&query=Kennedy%2C+D">D. Kennedy</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A">A. Edelen</a>, <a href="/search/physics?searchtype=author&query=Lalchand%2C+V+R">V. R. Lalchand</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">D. Ratner</a>, <a href="/search/physics?searchtype=author&query=Duris%2C+J">J. Duris</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="2009.03566v1-abstract-short" style="display: inline;"> High-dimensional optimization is a critical challenge for operating large-scale scientific facilities. We apply a physics-informed Gaussian process (GP) optimizer to tune a complex system by conducting efficient global search. Typical GP models learn from past observations to make predictions, but this reduces their applicability to new systems where archive data is not available. Instead, here we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.03566v1-abstract-full').style.display = 'inline'; document.getElementById('2009.03566v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.03566v1-abstract-full" style="display: none;"> High-dimensional optimization is a critical challenge for operating large-scale scientific facilities. We apply a physics-informed Gaussian process (GP) optimizer to tune a complex system by conducting efficient global search. Typical GP models learn from past observations to make predictions, but this reduces their applicability to new systems where archive data is not available. Instead, here we use a fast approximate model from physics simulations to design the GP model. The GP is then employed to make inferences from sequential online observations in order to optimize the system. Simulation and experimental studies were carried out to demonstrate the method for online control of a storage ring. We show that the physics-informed GP outperforms current routinely used online optimizers in terms of convergence speed, and robustness on this task. The ability to inform the machine-learning model with physics may have wide applications in science. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.03566v1-abstract-full').style.display = 'none'; document.getElementById('2009.03566v1-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> 8 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Accel. Beams 24, 072802 (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.09913">arXiv:2006.09913</a> <span> [<a href="https://arxiv.org/pdf/2006.09913">pdf</a>, <a href="https://arxiv.org/format/2006.09913">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Introduction to Machine Learning for Accelerator Physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</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="2006.09913v1-abstract-short" style="display: inline;"> This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of ML through a simple example of linear regression, including a probabilistic perspective to introduce the concepts of maximum likelihood estimation (MLE) and maximum a priori (MAP) estimation. We then apply the concepts t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.09913v1-abstract-full').style.display = 'inline'; document.getElementById('2006.09913v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.09913v1-abstract-full" style="display: none;"> This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of ML through a simple example of linear regression, including a probabilistic perspective to introduce the concepts of maximum likelihood estimation (MLE) and maximum a priori (MAP) estimation. We then apply the concepts to examples of neural networks and logistic regression. Next we introduce non-parametric models and the kernel method and give a brief introduction to two other machine learning paradigms, unsupervised and reinforcement learning. Finally we close with example applications of ML at a free-electron laser. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.09913v1-abstract-full').style.display = 'none'; document.getElementById('2006.09913v1-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 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">16 pages, contribution to the CAS - CERN Accelerator School: Numerical Methods for Analysis, Design and Modelling of Particle Accelerators, 11-23 November 2018, Thessaloniki, Greece</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.06090">arXiv:1911.06090</a> <span> [<a href="https://arxiv.org/pdf/1911.06090">pdf</a>, <a href="https://arxiv.org/format/1911.06090">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-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/10.1038/s41598-020-66220-5">10.1038/s41598-020-66220-5 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Temporal X-ray Reconstruction using Temporal and Spectral Measurements at LCLS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Christie%2C+F">Florian Christie</a>, <a href="/search/physics?searchtype=author&query=Lutman%2C+A+A">Alberto Andrea Lutman</a>, <a href="/search/physics?searchtype=author&query=Ding%2C+Y">Yuantao Ding</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+Z">Zhirong Huang</a>, <a href="/search/physics?searchtype=author&query=Jhalani%2C+V+A">Vatsal A. Jhalani</a>, <a href="/search/physics?searchtype=author&query=Krzywinski%2C+J">Jacek Krzywinski</a>, <a href="/search/physics?searchtype=author&query=Maxwell%2C+T+J">Timothy John Maxwell</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=R%C3%B6nsch-Schulenburg%2C+J">Juliane R枚nsch-Schulenburg</a>, <a href="/search/physics?searchtype=author&query=Vogt%2C+M">Mathias Vogt</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="1911.06090v2-abstract-short" style="display: inline;"> Transverse deflecting structures (TDS) are widely used in accelerator physics to measure the longitudinal density of particle bunches. When used in combination with a dispersive section, the whole longitudinal phase space density can be imaged. At the Linac Coherent Light Source (LCLS), the installation of such a device downstream of the undulators enables the reconstruction of the X-ray temporal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06090v2-abstract-full').style.display = 'inline'; document.getElementById('1911.06090v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.06090v2-abstract-full" style="display: none;"> Transverse deflecting structures (TDS) are widely used in accelerator physics to measure the longitudinal density of particle bunches. When used in combination with a dispersive section, the whole longitudinal phase space density can be imaged. At the Linac Coherent Light Source (LCLS), the installation of such a device downstream of the undulators enables the reconstruction of the X-ray temporal intensity profile by comparing longitudinal phase space distributions with lasing on and lasing off. However, the resolution of this TDS is limited to around 1 fs rms (root mean square), and therefore, it is not possible to resolve single self-amplified spontaneous emission (SASE) spikes within one X-ray photon pulse. By combining the power spectrum from a high resolution photon spectrometer and the temporal structure from the TDS, the overall resolution is enhanced, thus allowing the observation of temporal, single SASE spikes. The combined data from the spectrometer and the TDS is analyzed using an iterative algorithm to obtain the actual intensity profile. In this paper, we present some improvements to the reconstruction algorithm as well as real data taken at LCLS. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06090v2-abstract-full').style.display = 'none'; document.getElementById('1911.06090v2-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 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">Report number:</span> DESY 19-198 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Scientific Reports 10, 9799 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.01538">arXiv:1911.01538</a> <span> [<a href="https://arxiv.org/pdf/1911.01538">pdf</a>, <a href="https://arxiv.org/format/1911.01538">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-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> </div> </div> <p class="title is-5 mathjax"> Online tuning and light source control using a physics-informed Gaussian process Adi </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Hanuka%2C+A">A. Hanuka</a>, <a href="/search/physics?searchtype=author&query=Duris%2C+J">J. Duris</a>, <a href="/search/physics?searchtype=author&query=Shtalenkova%2C+J">J. Shtalenkova</a>, <a href="/search/physics?searchtype=author&query=Kennedy%2C+D">D. Kennedy</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A">A. Edelen</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">D. Ratner</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+X">X. Huang</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="1911.01538v1-abstract-short" style="display: inline;"> Operating large-scale scientific facilities often requires fast tuning and robust control in a high dimensional space. In this paper we introduce a new physics-informed optimization algorithm based on Gaussian process regression. Our method takes advantage of the existing domain knowledge in the form of realizations of a physics model of the observed system. We have applied a physics-informed Gaus… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.01538v1-abstract-full').style.display = 'inline'; document.getElementById('1911.01538v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.01538v1-abstract-full" style="display: none;"> Operating large-scale scientific facilities often requires fast tuning and robust control in a high dimensional space. In this paper we introduce a new physics-informed optimization algorithm based on Gaussian process regression. Our method takes advantage of the existing domain knowledge in the form of realizations of a physics model of the observed system. We have applied a physics-informed Gaussian Process method experimentally at the SPEAR3 storage ring to demonstrate online accelerator optimization. This method outperforms Gaussian Process trained on data as well as the standard approach routinely used for operation, in terms of convergence speed and optimal point. The proposed method could be applicable to automatic tuning and control of other complex systems, without a prerequisite for any observed data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.01538v1-abstract-full').style.display = 'none'; document.getElementById('1911.01538v1-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 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> https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_85.pdf </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.11926">arXiv:1910.11926</a> <span> [<a href="https://arxiv.org/pdf/1910.11926">pdf</a>, <a href="https://arxiv.org/format/1910.11926">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-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/PhysRevAccelBeams.23.022803">10.1103/PhysRevAccelBeams.23.022803 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Mapping Photocathode Quantum Efficiency with Ghost Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Kabra%2C+K">K. Kabra</a>, <a href="/search/physics?searchtype=author&query=Li%2C+S">S. Li</a>, <a href="/search/physics?searchtype=author&query=Cropp%2C+F">F. Cropp</a>, <a href="/search/physics?searchtype=author&query=Lane%2C+T+J">T. J. Lane</a>, <a href="/search/physics?searchtype=author&query=Musumeci%2C+P">P. Musumeci</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">D. Ratner</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="1910.11926v1-abstract-short" style="display: inline;"> Measuring the quantum efficiency (QE) map of a photocathode injector typically requires laser scanning, an invasive operation that involves modifying the injector laser focus and rastering the focused laser spot across the photocathode surface. Raster scanning interrupts normal operation and takes considerable time to setup. In this paper, we demonstrate a novel method of measuring the QE map usin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.11926v1-abstract-full').style.display = 'inline'; document.getElementById('1910.11926v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.11926v1-abstract-full" style="display: none;"> Measuring the quantum efficiency (QE) map of a photocathode injector typically requires laser scanning, an invasive operation that involves modifying the injector laser focus and rastering the focused laser spot across the photocathode surface. Raster scanning interrupts normal operation and takes considerable time to setup. In this paper, we demonstrate a novel method of measuring the QE map using a ghost imaging framework that correlates the injector laser spatial variation over time with the total charge yield. Ghost imaging enables passive, real-time monitoring of the QE map without manually modifying the injector laser or interrupting injector operation. We first demonstrate the method at the UCLA Pegasus photoinjector with the help of a digital micromirror device (DMD) and a piezoelectric mirror to increase our control of the overall transverse variance of the illumination profile. The reconstruction algorithm parameters are fine-tuned using simulations and the results are validated against the ground truth map acquired using the traditional rastering method. Finally, we apply the technique to data acquired parasitically from the LCLS photoinjector, showing the feasibility of this method to retrieve a QE map without interrupting normal operation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.11926v1-abstract-full').style.display = 'none'; document.getElementById('1910.11926v1-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 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">9 pages, 10 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Accel. Beams 23, 022803 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.07441">arXiv:1909.07441</a> <span> [<a href="https://arxiv.org/pdf/1909.07441">pdf</a>, <a href="https://arxiv.org/format/1909.07441">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="Optics">physics.optics</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/10.1039/C9CP03951A">10.1039/C9CP03951A <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Attosecond Transient Absorption Spooktroscopy: a ghost imaging approach to ultrafast absorption spectroscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Driver%2C+T">Taran Driver</a>, <a href="/search/physics?searchtype=author&query=Li%2C+S">Siqi Li</a>, <a href="/search/physics?searchtype=author&query=Champenois%2C+E+G">Elio G. Champenois</a>, <a href="/search/physics?searchtype=author&query=Duris%2C+J">Joseph Duris</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Lane%2C+T">TJ Lane</a>, <a href="/search/physics?searchtype=author&query=Rosenberger%2C+P">Philipp Rosenberger</a>, <a href="/search/physics?searchtype=author&query=Al-Haddad%2C+A">Andre Al-Haddad</a>, <a href="/search/physics?searchtype=author&query=Averbukh%2C+V">Vitali Averbukh</a>, <a href="/search/physics?searchtype=author&query=Barnard%2C+T">Toby Barnard</a>, <a href="/search/physics?searchtype=author&query=Berrah%2C+N">Nora Berrah</a>, <a href="/search/physics?searchtype=author&query=Bostedt%2C+C">Christoph Bostedt</a>, <a href="/search/physics?searchtype=author&query=Bucksbaum%2C+P+H">Philip H. Bucksbaum</a>, <a href="/search/physics?searchtype=author&query=Coffee%2C+R">Ryan Coffee</a>, <a href="/search/physics?searchtype=author&query=DiMauro%2C+L+F">Louis F. DiMauro</a>, <a href="/search/physics?searchtype=author&query=Fang%2C+L">Li Fang</a>, <a href="/search/physics?searchtype=author&query=Garratt%2C+D">Douglas Garratt</a>, <a href="/search/physics?searchtype=author&query=Gatton%2C+A">Averell Gatton</a>, <a href="/search/physics?searchtype=author&query=Guo%2C+Z">Zhaoheng Guo</a>, <a href="/search/physics?searchtype=author&query=Hartmann%2C+G">Gregor Hartmann</a>, <a href="/search/physics?searchtype=author&query=Haxton%2C+D">Daniel Haxton</a>, <a href="/search/physics?searchtype=author&query=Helml%2C+W">Wolfram Helml</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+Z">Zhirong Huang</a>, <a href="/search/physics?searchtype=author&query=LaForge%2C+A">Aaron LaForge</a>, <a href="/search/physics?searchtype=author&query=Kamalov%2C+A">Andrei Kamalov</a> , et al. (16 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="1909.07441v1-abstract-short" style="display: inline;"> The recent demonstration of isolated attosecond pulses from an X-ray free-electron laser (XFEL) opens the possibility for probing ultrafast electron dynamics at X-ray wavelengths. An established experimental method for probing ultrafast dynamics is X-ray transient absorption spectroscopy, where the X-ray absorption spectrum is measured by scanning the central photon energy and recording the result… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.07441v1-abstract-full').style.display = 'inline'; document.getElementById('1909.07441v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.07441v1-abstract-full" style="display: none;"> The recent demonstration of isolated attosecond pulses from an X-ray free-electron laser (XFEL) opens the possibility for probing ultrafast electron dynamics at X-ray wavelengths. An established experimental method for probing ultrafast dynamics is X-ray transient absorption spectroscopy, where the X-ray absorption spectrum is measured by scanning the central photon energy and recording the resultant photoproducts. The spectral bandwidth inherent to attosecond pulses is wide compared to the resonant features typically probed, which generally precludes the application of this technique in the attosecond regime. In this paper we propose and demonstrate a new technique to conduct transient absorption spectroscopy with broad bandwidth attosecond pulses with the aid of ghost imaging, recovering sub-bandwidth resolution in photoproduct-based absorption measurements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.07441v1-abstract-full').style.display = 'none'; document.getElementById('1909.07441v1-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 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">13 pages, 3 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/1909.05963">arXiv:1909.05963</a> <span> [<a href="https://arxiv.org/pdf/1909.05963">pdf</a>, <a href="https://arxiv.org/format/1909.05963">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-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 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/PhysRevLett.124.124801">10.1103/PhysRevLett.124.124801 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Bayesian optimization of a free-electron laser </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Duris%2C+J">Joseph Duris</a>, <a href="/search/physics?searchtype=author&query=Kennedy%2C+D">Dylan Kennedy</a>, <a href="/search/physics?searchtype=author&query=Hanuka%2C+A">Adi Hanuka</a>, <a href="/search/physics?searchtype=author&query=Shtalenkova%2C+J">Jane Shtalenkova</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+A">Auralee Edelen</a>, <a href="/search/physics?searchtype=author&query=Egger%2C+A">Adam Egger</a>, <a href="/search/physics?searchtype=author&query=Cope%2C+T">Tyler Cope</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</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="1909.05963v1-abstract-short" style="display: inline;"> The Linac Coherent Light Source changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to transport optics tuning to optimize groups of quadrupole magnets. We use a Gaussian process to provide a probabilistic model of the machine response with respect to control parameters from… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.05963v1-abstract-full').style.display = 'inline'; document.getElementById('1909.05963v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.05963v1-abstract-full" style="display: none;"> The Linac Coherent Light Source changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to transport optics tuning to optimize groups of quadrupole magnets. We use a Gaussian process to provide a probabilistic model of the machine response with respect to control parameters from a modest number of samples. Subsequent samples are selected during optimization using a statistical test combining the model prediction and uncertainty. The model parameters are fit from archived scans, and correlations between devices are added from a simple beam transport model. The result is a sample-efficient optimization routine, which we show significantly outperforms existing optimizers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.05963v1-abstract-full').style.display = 'none'; document.getElementById('1909.05963v1-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 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">6 pages, 3 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Lett. 124, 124801 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1907.12178">arXiv:1907.12178</a> <span> [<a href="https://arxiv.org/pdf/1907.12178">pdf</a>, <a href="https://arxiv.org/format/1907.12178">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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/10.1364/OE.379503">10.1364/OE.379503 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> What are the advantages of ghost imaging? Multiplexing for x-ray and electron imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Lane%2C+T+J">Thomas J. Lane</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</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="1907.12178v2-abstract-short" style="display: inline;"> Ghost imaging, Fourier transform spectroscopy, and the newly developed Hadamard transform crystallography are all examples of multiplexing measurement strategies. Multiplexed experiments are performed by measuring multiple points in space, time, or energy simultaneously. This contrasts to the usual method of systematically scanning single points. How do multiplexed measurements work and when they… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.12178v2-abstract-full').style.display = 'inline'; document.getElementById('1907.12178v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1907.12178v2-abstract-full" style="display: none;"> Ghost imaging, Fourier transform spectroscopy, and the newly developed Hadamard transform crystallography are all examples of multiplexing measurement strategies. Multiplexed experiments are performed by measuring multiple points in space, time, or energy simultaneously. This contrasts to the usual method of systematically scanning single points. How do multiplexed measurements work and when they are advantageous? Here we address these questions with a focus on applications involving x-rays or electrons. We present a quantitative framework for analyzing the expected error and radiation dose of different measurement scheme that enables comparison. We conclude that in very specific situations, multiplexing can offer improvements in resolution and signal-to-noise. If the signal has a sparse representation, these advantages become more general and dramatic, and further less radiation can be used to complete a measurement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.12178v2-abstract-full').style.display = 'none'; document.getElementById('1907.12178v2-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 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 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">13 pages, 4 figures, preprint</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.03172">arXiv:1811.03172</a> <span> [<a href="https://arxiv.org/pdf/1811.03172">pdf</a>, <a href="https://arxiv.org/ps/1811.03172">ps</a>, <a href="https://arxiv.org/format/1811.03172">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Accelerator Physics">physics.acc-ph</span> </div> </div> <p class="title is-5 mathjax"> Opportunities in Machine Learning for Particle Accelerators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Edelen%2C+A">Auralee Edelen</a>, <a href="/search/physics?searchtype=author&query=Mayes%2C+C">Christopher Mayes</a>, <a href="/search/physics?searchtype=author&query=Bowring%2C+D">Daniel Bowring</a>, <a href="/search/physics?searchtype=author&query=Ratner%2C+D">Daniel Ratner</a>, <a href="/search/physics?searchtype=author&query=Adelmann%2C+A">Andreas Adelmann</a>, <a href="/search/physics?searchtype=author&query=Ischebeck%2C+R">Rasmus Ischebeck</a>, <a href="/search/physics?searchtype=author&query=Snuverink%2C+J">Jochem Snuverink</a>, <a href="/search/physics?searchtype=author&query=Agapov%2C+I">Ilya Agapov</a>, <a href="/search/physics?searchtype=author&query=Kammering%2C+R">Raimund Kammering</a>, <a href="/search/physics?searchtype=author&query=Edelen%2C+J">Jonathan Edelen</a>, <a href="/search/physics?searchtype=author&query=Bazarov%2C+I">Ivan Bazarov</a>, <a href="/search/physics?searchtype=author&query=Valentino%2C+G">Gianluca Valentino</a>, <a href="/search/physics?searchtype=author&query=Wenninger%2C+J">Jorg Wenninger</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="1811.03172v1-abstract-short" style="display: inline;"> Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.03172v1-abstract-full').style.display = 'inline'; document.getElementById('1811.03172v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.03172v1-abstract-full" style="display: none;"> Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.03172v1-abstract-full').style.display = 'none'; document.getElementById('1811.03172v1-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, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </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 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