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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/2411.08587">arXiv:2411.08587</a> <span> [<a href="https://arxiv.org/pdf/2411.08587">pdf</a>, <a href="https://arxiv.org/format/2411.08587">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nevin%2C+R">Rebecca Nevin</a>, <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B+D">Brian D. Nord</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="2411.08587v1-abstract-short" style="display: inline;"> Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER). Our method foc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08587v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08587v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08587v1-abstract-full" style="display: none;"> Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER). Our method focuses on both zero-dimensional (0D) and two-dimensional (2D) data, to explore how the UQ methods function for different data dimensionalities. We investigate uncertainty injected on the input and output variables and include a method to propagate uncertainty in the case of input uncertainty so that we can compare the predicted aleatoric uncertainty to the known values. We experiment with three levels of noise. The aleatoric uncertainty predicted across all models and experiments scales with the injected noise level. However, the predicted uncertainty is miscalibrated to $\rm{std}(蟽_{\rm al})$ with the true uncertainty for half of the DE experiments and almost all of the DER experiments. The predicted uncertainty is the least accurate for both UQ methods for the 2D input uncertainty experiment and the high-noise level. While these results do not apply to more complex data, they highlight that further research on post-facto calibration for these methods would be beneficial, particularly for high-noise and high-dimensional settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08587v1-abstract-full').style.display = 'none'; document.getElementById('2411.08587v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Accepted to the Machine Learning for Physical Sciences workshop at NeurIPS 2024; 11 pages, 2 figures, 2 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-24-0521-CSAID-PPD FERMILAB-CONF-24-0521-CSAID-PPD FERMILAB-CONF-24-0521-CSAID-PPD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.03334">arXiv:2411.03334</a> <span> [<a href="https://arxiv.org/pdf/2411.03334">pdf</a>, <a href="https://arxiv.org/format/2411.03334">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 Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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> <p class="title is-5 mathjax"> Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Agarwal%2C+S">Shrihan Agarwal</a>, <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B+D">Brian D. Nord</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="2411.03334v1-abstract-short" style="display: inline;"> Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03334v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03334v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03334v1-abstract-full" style="display: none;"> Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03334v1-abstract-full').style.display = 'none'; document.getElementById('2411.03334v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Accepted to the Machine Learning for Physical Sciences workshop at NeurIPS 2024; 24 pages, 2 figures, 4 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-24-0523-CSAID-PPD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16347">arXiv:2410.16347</a> <span> [<a href="https://arxiv.org/pdf/2410.16347">pdf</a>, <a href="https://arxiv.org/format/2410.16347">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 Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Swierc%2C+P">Paxson Swierc</a>, <a href="/search/cs?searchtype=author&query=Tamargo-Arizmendi%2C+M">Marcos Tamargo-Arizmendi</a>, <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B+D">Brian D. Nord</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="2410.16347v1-abstract-short" style="display: inline;"> Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of strong lensing data. However, NPE has not been demonstrated to perform well on out-of-domain target data -- e.g., when trained on simulated data and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16347v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16347v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16347v1-abstract-full" style="display: none;"> Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of strong lensing data. However, NPE has not been demonstrated to perform well on out-of-domain target data -- e.g., when trained on simulated data and then applied to real, observational data. In this work, we perform the first study of the efficacy of NPE in combination with unsupervised domain adaptation (UDA). The source domain is noiseless, and the target domain has noise mimicking modern cosmology surveys. We find that combining UDA and NPE improves the accuracy of the inference by 1-2 orders of magnitude and significantly improves the posterior coverage over an NPE model without UDA. We anticipate that this combination of approaches will help enable future applications of NPE models to real observational data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16347v1-abstract-full').style.display = 'none'; document.getElementById('2410.16347v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">20 pages, 2 figures, 2 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-24-0444-CSAID-PPD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.11772">arXiv:2409.11772</a> <span> [<a href="https://arxiv.org/pdf/2409.11772">pdf</a>, <a href="https://arxiv.org/format/2409.11772">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</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"> Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samudre%2C+A">Ashwin Samudre</a>, <a href="/search/cs?searchtype=author&query=Petrache%2C+M">Mircea Petrache</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B+D">Brian D. Nord</a>, <a href="/search/cs?searchtype=author&query=Trivedi%2C+S">Shubhendu Trivedi</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="2409.11772v1-abstract-short" style="display: inline;"> There has been much recent interest in designing symmetry-aware neural networks (NNs) exhibiting relaxed equivariance. Such NNs aim to interpolate between being exactly equivariant and being fully flexible, affording consistent performance benefits. In a separate line of work, certain structured parameter matrices -- those with displacement structure, characterized by low displacement rank (LDR) -… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11772v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11772v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11772v1-abstract-full" style="display: none;"> There has been much recent interest in designing symmetry-aware neural networks (NNs) exhibiting relaxed equivariance. Such NNs aim to interpolate between being exactly equivariant and being fully flexible, affording consistent performance benefits. In a separate line of work, certain structured parameter matrices -- those with displacement structure, characterized by low displacement rank (LDR) -- have been used to design small-footprint NNs. Displacement structure enables fast function and gradient evaluation, but permits accurate approximations via compression primarily to classical convolutional neural networks (CNNs). In this work, we propose a general framework -- based on a novel construction of symmetry-based structured matrices -- to build approximately equivariant NNs with significantly reduced parameter counts. Our framework integrates the two aforementioned lines of work via the use of so-called Group Matrices (GMs), a forgotten precursor to the modern notion of regular representations of finite groups. GMs allow the design of structured matrices -- resembling LDR matrices -- which generalize the linear operations of a classical CNN from cyclic groups to general finite groups and their homogeneous spaces. We show that GMs can be employed to extend all the elementary operations of CNNs to general discrete groups. Further, the theory of structured matrices based on GMs provides a generalization of LDR theory focussed on matrices with cyclic structure, providing a tool for implementing approximate equivariance for discrete groups. We test GM-based architectures on a variety of tasks in the presence of relaxed symmetry. We report that our framework consistently performs competitively compared to approximately equivariant NNs, and other structured matrix-based compression frameworks, sometimes with a one or two orders of magnitude lower parameter count. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11772v1-abstract-full').style.display = 'none'; document.getElementById('2409.11772v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">20 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.18094">arXiv:2311.18094</a> <span> [<a href="https://arxiv.org/pdf/2311.18094">pdf</a>, <a href="https://arxiv.org/format/2311.18094">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 Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</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> <p class="title is-5 mathjax"> Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Terranova%2C+F">Franco Terranova</a>, <a href="/search/cs?searchtype=author&query=Voetberg%2C+M">M. Voetberg</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Pagul%2C+A">Amanda Pagul</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="2311.18094v1-abstract-short" style="display: inline;"> Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimiz… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18094v1-abstract-full').style.display = 'inline'; document.getElementById('2311.18094v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.18094v1-abstract-full" style="display: none;"> Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18094v1-abstract-full').style.display = 'none'; document.getElementById('2311.18094v1-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 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 5 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-23-654-CSAID </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.01588">arXiv:2311.01588</a> <span> [<a href="https://arxiv.org/pdf/2311.01588">pdf</a>, <a href="https://arxiv.org/format/2311.01588">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</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> <p class="title is-5 mathjax"> Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Roncoli%2C+A">Andrea Roncoli</a>, <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Voetberg%2C+M">Maggie Voetberg</a>, <a href="/search/cs?searchtype=author&query=Villaescusa-Navarro%2C+F">Francisco Villaescusa-Navarro</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</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="2311.01588v3-abstract-short" style="display: inline;"> Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.01588v3-abstract-full').style.display = 'inline'; document.getElementById('2311.01588v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.01588v3-abstract-full" style="display: none;"> Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $蠂^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.01588v3-abstract-full').style.display = 'none'; document.getElementById('2311.01588v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 9 pages, 2 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-23-644-CSAID </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.12528">arXiv:2310.12528</a> <span> [<a href="https://arxiv.org/pdf/2310.12528">pdf</a>, <a href="https://arxiv.org/format/2310.12528">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 Methods for Astrophysics">astro-ph.IM</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"> Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huppenkothen%2C+D">D. Huppenkothen</a>, <a href="/search/cs?searchtype=author&query=Ntampaka%2C+M">M. Ntampaka</a>, <a href="/search/cs?searchtype=author&query=Ho%2C+M">M. Ho</a>, <a href="/search/cs?searchtype=author&query=Fouesneau%2C+M">M. Fouesneau</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</a>, <a href="/search/cs?searchtype=author&query=Peek%2C+J+E+G">J. E. G. Peek</a>, <a href="/search/cs?searchtype=author&query=Walmsley%2C+M">M. Walmsley</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+J+F">J. F. Wu</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">C. Avestruz</a>, <a href="/search/cs?searchtype=author&query=Buck%2C+T">T. Buck</a>, <a href="/search/cs?searchtype=author&query=Brescia%2C+M">M. Brescia</a>, <a href="/search/cs?searchtype=author&query=Finkbeiner%2C+D+P">D. P. Finkbeiner</a>, <a href="/search/cs?searchtype=author&query=Goulding%2C+A+D">A. D. Goulding</a>, <a href="/search/cs?searchtype=author&query=Kacprzak%2C+T">T. Kacprzak</a>, <a href="/search/cs?searchtype=author&query=Melchior%2C+P">P. Melchior</a>, <a href="/search/cs?searchtype=author&query=Pasquato%2C+M">M. Pasquato</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">N. Ramachandra</a>, <a href="/search/cs?searchtype=author&query=Ting%2C+Y+-">Y. -S. Ting</a>, <a href="/search/cs?searchtype=author&query=van+de+Ven%2C+G">G. van de Ven</a>, <a href="/search/cs?searchtype=author&query=Villar%2C+S">S. Villar</a>, <a href="/search/cs?searchtype=author&query=Villar%2C+V+A">V. A. Villar</a>, <a href="/search/cs?searchtype=author&query=Zinger%2C+E">E. Zinger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.12528v1-abstract-short" style="display: inline;"> Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12528v1-abstract-full').style.display = 'inline'; document.getElementById('2310.12528v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12528v1-abstract-full" style="display: none;"> Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12528v1-abstract-full').style.display = 'none'; document.getElementById('2310.12528v1-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.08734">arXiv:2306.08734</a> <span> [<a href="https://arxiv.org/pdf/2306.08734">pdf</a>, <a href="https://arxiv.org/format/2306.08734">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> WavPool: A New Block for Deep Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=McDermott%2C+S+D">Samuel D. McDermott</a>, <a href="/search/cs?searchtype=author&query=Voetberg%2C+M">M. Voetberg</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</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="2306.08734v1-abstract-short" style="display: inline;"> Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks. In this work, we introduce a new, wavelet-transform-based network architecture that we call the multi-resolution perceptron: by adding a pooling layer, we create a new network block, the WavPool. The first step of the multi-resolution perceptron is transformin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.08734v1-abstract-full').style.display = 'inline'; document.getElementById('2306.08734v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.08734v1-abstract-full" style="display: none;"> Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks. In this work, we introduce a new, wavelet-transform-based network architecture that we call the multi-resolution perceptron: by adding a pooling layer, we create a new network block, the WavPool. The first step of the multi-resolution perceptron is transforming the data into its multi-resolution decomposition form by convolving the input data with filters of fixed coefficients but increasing size. Following image processing techniques, we are able to make scale and spatial information simultaneously accessible to the network without increasing the size of the data vector. WavPool outperforms a similar multilayer perceptron while using fewer parameters, and outperforms a comparable convolutional neural network by ~ 10% on relative accuracy on CIFAR-10. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.08734v1-abstract-full').style.display = 'none'; document.getElementById('2306.08734v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">8+8 pages, 3+3 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-23-278-CSAID </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.02005">arXiv:2302.02005</a> <span> [<a href="https://arxiv.org/pdf/2302.02005">pdf</a>, <a href="https://arxiv.org/format/2302.02005">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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> </div> </div> <p class="title is-5 mathjax"> DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">A. 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Lewis%2C+A">A. Lewis</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">K. Pedro</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">S. Madireddy</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">G. N. Perdue</a>, <a href="/search/cs?searchtype=author&query=Wild%2C+S+M">S. M. Wild</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.02005v2-abstract-short" style="display: inline;"> Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, \textit{DeepAstroUDA}, as an approach to o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02005v2-abstract-full').style.display = 'inline'; document.getElementById('2302.02005v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.02005v2-abstract-full" style="display: none;"> Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, \textit{DeepAstroUDA}, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities ($3$-class and $10$-class problems), with anomaly detection: 1) datasets created after different numbers of observing years from a single survey (LSST mock data of $1$ and $10$ years of observations); 2) data from different surveys (SDSS and DECaLS); and 3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of domain adaptation between very discrepant observational datasets. \textit{DeepAstroUDA} is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to $40\%$ on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02005v2-abstract-full').style.display = 'none'; document.getElementById('2302.02005v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in Machine Learning Science and Technology (MLST); 24 pages, 14 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-23-034-CSAID </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.10305">arXiv:2211.10305</a> <span> [<a href="https://arxiv.org/pdf/2211.10305">pdf</a>, <a href="https://arxiv.org/format/2211.10305">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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"> Neural Inference of Gaussian Processes for Time Series Data of Quasars </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Danilov%2C+E">Egor Danilov</a>, <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</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="2211.10305v1-abstract-short" style="display: inline;"> The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is to interpolate a time series with a Damped Random Walk (DRW) model, in which the spectrum is inferred using Maximum Likelihood Estimation (MLE). However, the DRW model does not describe the smoothness of the time series,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10305v1-abstract-full').style.display = 'inline'; document.getElementById('2211.10305v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.10305v1-abstract-full" style="display: none;"> The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is to interpolate a time series with a Damped Random Walk (DRW) model, in which the spectrum is inferred using Maximum Likelihood Estimation (MLE). However, the DRW model does not describe the smoothness of the time series, and MLE faces many problems in terms of optimization and numerical precision. In this work, we introduce a new stochastic model that we call $\textit{Convolved Damped Random Walk}$ (CDRW). This model introduces a concept of smoothness to a DRW, which enables it to describe quasar spectra completely. We also introduce a new method of inference of Gaussian process parameters, which we call $\textit{Neural Inference}$. This method uses the powers of state-of-the-art neural networks to improve the conventional MLE inference technique. In our experiments, the Neural Inference method results in significant improvement over the baseline MLE (RMSE: $0.318 \rightarrow 0.205$, $0.464 \rightarrow 0.444$). Moreover, the combination of both the CDRW model and Neural Inference significantly outperforms the baseline DRW and MLE in interpolating a typical quasar light curve ($蠂^2$: $0.333 \rightarrow 0.998$, $2.695 \rightarrow 0.981$). The code is published on GitHub. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10305v1-abstract-full').style.display = 'none'; document.getElementById('2211.10305v1-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 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Machine Learning and the Physical Sciences workshop, NeurIPS 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.00677">arXiv:2211.00677</a> <span> [<a href="https://arxiv.org/pdf/2211.00677">pdf</a>, <a href="https://arxiv.org/format/2211.00677">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Lewis%2C+A">Ashia Lewis</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">Kevin Pedro</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">Sandeep Madireddy</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">Gabriel N. Perdue</a>, <a href="/search/cs?searchtype=author&query=Wild%2C+S+M">Stefan M. Wild</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="2211.00677v3-abstract-short" style="display: inline;"> In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capabl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.00677v3-abstract-full').style.display = 'inline'; document.getElementById('2211.00677v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.00677v3-abstract-full" style="display: none;"> In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.00677v3-abstract-full').style.display = 'none'; document.getElementById('2211.00677v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">3 figures, 1 table; accepted to Machine Learning and the Physical Sciences - Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-22-791-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.00024">arXiv:2211.00024</a> <span> [<a href="https://arxiv.org/pdf/2211.00024">pdf</a>, <a href="https://arxiv.org/format/2211.00024">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="Instrumentation and Methods for Astrophysics">astro-ph.IM</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/acc444">10.1088/2632-2153/acc444 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A robust estimator of mutual information for deep learning interpretability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Piras%2C+D">Davide Piras</a>, <a href="/search/cs?searchtype=author&query=Peiris%2C+H+V">Hiranya V. Peiris</a>, <a href="/search/cs?searchtype=author&query=Pontzen%2C+A">Andrew Pontzen</a>, <a href="/search/cs?searchtype=author&query=Lucie-Smith%2C+L">Luisa Lucie-Smith</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+N">Ningyuan Guo</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</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="2211.00024v2-abstract-short" style="display: inline;"> We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced $``$Jimmie$"$), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficien… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.00024v2-abstract-full').style.display = 'inline'; document.getElementById('2211.00024v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.00024v2-abstract-full" style="display: none;"> We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced $``$Jimmie$"$), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established mutual information estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train deep learning models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.00024v2-abstract-full').style.display = 'none'; document.getElementById('2211.00024v2-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">30 pages, 8 figures. Minor changes to match version accepted for publication in Machine Learning: Science and Technology. GMM-MI available at https://github.com/dpiras/GMM-MI</span> </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, Volume 4, Number 2, 025006, April 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.03284">arXiv:2208.03284</a> <span> [<a href="https://arxiv.org/pdf/2208.03284">pdf</a>, <a href="https://arxiv.org/ps/2208.03284">ps</a>, <a href="https://arxiv.org/format/2208.03284">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.2172/1886020">10.2172/1886020 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Interpretable Uncertainty Quantification in AI for HEP </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+T+Y">Thomas Y. Chen</a>, <a href="/search/cs?searchtype=author&query=Dey%2C+B">Biprateep Dey</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+A">Aishik Ghosh</a>, <a href="/search/cs?searchtype=author&query=Kagan%2C+M">Michael Kagan</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Ramachandra%2C+N">Nesar Ramachandra</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.03284v3-abstract-short" style="display: inline;"> Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.03284v3-abstract-full').style.display = 'inline'; document.getElementById('2208.03284v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.03284v3-abstract-full" style="display: none;"> Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake, but also on the methods we use for that task. For artificial intelligence (AI) applications in HEP, there are several areas where interpretable methods for UQ are essential, including inference, simulation, and control/decision-making. There exist some methods for each of these areas, but they have not yet been demonstrated to be as trustworthy as more traditional approaches currently employed in physics (e.g., non-AI frequentist and Bayesian methods). Shedding light on the questions above requires additional understanding of the interplay of AI systems and uncertainty quantification. We briefly discuss the existing methods in each area and relate them to tasks across HEP. We then discuss recommendations for avenues to pursue to develop the necessary techniques for reliable widespread usage of AI with UQ over the next decade. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.03284v3-abstract-full').style.display = 'none'; document.getElementById('2208.03284v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-FN-1179-SCD; arXiv:2208.03284 oai:inspirehep.net:2132723 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.08827">arXiv:2203.08827</a> <span> [<a href="https://arxiv.org/pdf/2203.08827">pdf</a>, <a href="https://arxiv.org/format/2203.08827">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</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/10.1103/PhysRevD.105.103533">10.1103/PhysRevD.105.103533 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Discovering the building blocks of dark matter halo density profiles with neural networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lucie-Smith%2C+L">Luisa Lucie-Smith</a>, <a href="/search/cs?searchtype=author&query=Peiris%2C+H+V">Hiranya V. Peiris</a>, <a href="/search/cs?searchtype=author&query=Pontzen%2C+A">Andrew Pontzen</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Thiyagalingam%2C+J">Jeyan Thiyagalingam</a>, <a href="/search/cs?searchtype=author&query=Piras%2C+D">Davide Piras</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.08827v2-abstract-short" style="display: inline;"> The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network model that is trained to learn the mapping from the raw density field containing each halo to the dark matter density profile. We show that the model recovers the widely-used Navarro-Frenk-White (NFW) profile out to the vir… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08827v2-abstract-full').style.display = 'inline'; document.getElementById('2203.08827v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.08827v2-abstract-full" style="display: none;"> The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network model that is trained to learn the mapping from the raw density field containing each halo to the dark matter density profile. We show that the model recovers the widely-used Navarro-Frenk-White (NFW) profile out to the virial radius, and can additionally describe the variability in the outer profile of the halos. The neural network architecture consists of a supervised encoder-decoder framework, which first compresses the density inputs into a low-dimensional latent representation, and then outputs $蟻(r)$ for any desired value of radius $r$. The latent representation contains all the information used by the model to predict the density profiles. This allows us to interpret the latent representation by quantifying the mutual information between the representation and the halos' ground-truth density profiles. A two-dimensional representation is sufficient to accurately model the density profiles up to the virial radius; however, a three-dimensional representation is required to describe the outer profiles beyond the virial radius. The additional dimension in the representation contains information about the infalling material in the outer profiles of dark matter halos, thus discovering the splashback boundary of halos without prior knowledge of the halos' dynamical history. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08827v2-abstract-full').style.display = 'none'; document.getElementById('2203.08827v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 6 figures. Minor changes to match version accepted for publication in PRD</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.08656">arXiv:2203.08656</a> <span> [<a href="https://arxiv.org/pdf/2203.08656">pdf</a>, <a href="https://arxiv.org/format/2203.08656">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Learning Representation for Bayesian Optimization with Collision-free Regularization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+F">Fengxue Zhang</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yuxin Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.08656v1-abstract-short" style="display: inline;"> Bayesian optimization has been challenged by datasets with large-scale, high-dimensional, and non-stationary characteristics, which are common in real-world scenarios. Recent works attempt to handle such input by applying neural networks ahead of the classical Gaussian process to learn a latent representation. We show that even with proper network design, such learned representation often leads to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08656v1-abstract-full').style.display = 'inline'; document.getElementById('2203.08656v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.08656v1-abstract-full" style="display: none;"> Bayesian optimization has been challenged by datasets with large-scale, high-dimensional, and non-stationary characteristics, which are common in real-world scenarios. Recent works attempt to handle such input by applying neural networks ahead of the classical Gaussian process to learn a latent representation. We show that even with proper network design, such learned representation often leads to collision in the latent space: two points with significantly different observations collide in the learned latent space, leading to degraded optimization performance. To address this issue, we propose LOCo, an efficient deep Bayesian optimization framework which employs a novel regularizer to reduce the collision in the learned latent space and encourage the mapping from the latent space to the objective value to be Lipschitz continuous. LOCo takes in pairs of data points and penalizes those too close in the latent space compared to their target space distance. We provide a rigorous theoretical justification for LOCo by inspecting the regret of this dynamic-embedding-based Bayesian optimization algorithm, where the neural network is iteratively retrained with the regularizer. Our empirical results demonstrate the effectiveness of LOCo on several synthetic and real-world benchmark Bayesian optimization tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08656v1-abstract-full').style.display = 'none'; document.getElementById('2203.08656v1-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 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">28 pages, 24 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/2203.08056">arXiv:2203.08056</a> <span> [<a href="https://arxiv.org/pdf/2203.08056">pdf</a>, <a href="https://arxiv.org/ps/2203.08056">ps</a>, <a href="https://arxiv.org/format/2203.08056">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Machine Learning and Cosmology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dvorkin%2C+C">Cora Dvorkin</a>, <a href="/search/cs?searchtype=author&query=Mishra-Sharma%2C+S">Siddharth Mishra-Sharma</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Villar%2C+V+A">V. Ashley Villar</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">Camille Avestruz</a>, <a href="/search/cs?searchtype=author&query=Bechtol%2C+K">Keith Bechtol</a>, <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Connolly%2C+A+J">Andrew J. Connolly</a>, <a href="/search/cs?searchtype=author&query=Garrison%2C+L+H">Lehman H. Garrison</a>, <a href="/search/cs?searchtype=author&query=Narayan%2C+G">Gautham Narayan</a>, <a href="/search/cs?searchtype=author&query=Villaescusa-Navarro%2C+F">Francisco Villaescusa-Navarro</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.08056v1-abstract-short" style="display: inline;"> Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning rem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08056v1-abstract-full').style.display = 'inline'; document.getElementById('2203.08056v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.08056v1-abstract-full" style="display: none;"> Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.08056v1-abstract-full').style.display = 'none'; document.getElementById('2203.08056v1-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 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Contribution to Snowmass 2021. 32 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.14299">arXiv:2112.14299</a> <span> [<a href="https://arxiv.org/pdf/2112.14299">pdf</a>, <a href="https://arxiv.org/format/2112.14299">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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> </div> </div> <p class="title is-5 mathjax"> DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">Aleksandra 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Kafkes%2C+D">Diana Kafkes</a>, <a href="/search/cs?searchtype=author&query=Snyder%2C+G">Gregory Snyder</a>, <a href="/search/cs?searchtype=author&query=S%C3%A1nchez%2C+F+J">F. Javier S谩nchez</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">Gabriel Nathan Perdue</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">Kevin Pedro</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">Sandeep Madireddy</a>, <a href="/search/cs?searchtype=author&query=Wild%2C+S+M">Stefan M. Wild</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="2112.14299v3-abstract-short" style="display: inline;"> With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the eff… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.14299v3-abstract-full').style.display = 'inline'; document.getElementById('2112.14299v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.14299v3-abstract-full" style="display: none;"> With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: 1) increased observational noise as represented by higher levels of Poisson noise and 2) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness. Without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise. Domain adaptation also increases by a factor of ~2.3 the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.14299v3-abstract-full').style.display = 'none'; document.getElementById('2112.14299v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 6 figures, 5 tables; accepted in MLST</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-21-767-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.00961">arXiv:2111.00961</a> <span> [<a href="https://arxiv.org/pdf/2111.00961">pdf</a>, <a href="https://arxiv.org/format/2111.00961">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Robustness of deep learning algorithms in astronomy -- galaxy morphology studies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">A. 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Kafkes%2C+D">D. Kafkes</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">G. N. Perdue</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">K. Pedro</a>, <a href="/search/cs?searchtype=author&query=Snyder%2C+G">G. Snyder</a>, <a href="/search/cs?searchtype=author&query=S%C3%A1nchez%2C+F+J">F. J. S谩nchez</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">S. Madireddy</a>, <a href="/search/cs?searchtype=author&query=Wild%2C+S+M">S. M. Wild</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</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="2111.00961v2-abstract-short" style="display: inline;"> Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.00961v2-abstract-full').style.display = 'inline'; document.getElementById('2111.00961v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.00961v2-abstract-full" style="display: none;"> Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.00961v2-abstract-full').style.display = 'none'; document.getElementById('2111.00961v2-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, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in: Fourth Workshop on Machine Learning and the Physical Sciences (35th Conference on Neural Information Processing Systems; NeurIPS2021); final version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-21-561-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.09761">arXiv:2106.09761</a> <span> [<a href="https://arxiv.org/pdf/2106.09761">pdf</a>, <a href="https://arxiv.org/format/2106.09761">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Resource Allocation with Graph Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cranmer%2C+M">Miles Cranmer</a>, <a href="/search/cs?searchtype=author&query=Melchior%2C+P">Peter Melchior</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.09761v1-abstract-short" style="display: inline;"> We present an approach for maximizing a global utility function by learning how to allocate resources in an unsupervised way. We expect interactions between allocation targets to be important and therefore propose to learn the reward structure for near-optimal allocation policies with a GNN. By relaxing the resource constraint, we can employ gradient-based optimization in contrast to more standard… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.09761v1-abstract-full').style.display = 'inline'; document.getElementById('2106.09761v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.09761v1-abstract-full" style="display: none;"> We present an approach for maximizing a global utility function by learning how to allocate resources in an unsupervised way. We expect interactions between allocation targets to be important and therefore propose to learn the reward structure for near-optimal allocation policies with a GNN. By relaxing the resource constraint, we can employ gradient-based optimization in contrast to more standard evolutionary algorithms. Our algorithm is motivated by a problem in modern astronomy, where one needs to select-based on limited initial information-among $10^9$ galaxies those whose detailed measurement will lead to optimal inference of the composition of the universe. Our technique presents a way of flexibly learning an allocation strategy by only requiring forward simulators for the physics of interest and the measurement process. We anticipate that our technique will also find applications in a range of resource allocation problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.09761v1-abstract-full').style.display = 'none'; document.getElementById('2106.09761v1-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, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to PMLR/contributed oral at NeurIPS 2020 Pre-registration Workshop. Code at https://github.com/MilesCranmer/gnn_resource_allocation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.01373">arXiv:2103.01373</a> <span> [<a href="https://arxiv.org/pdf/2103.01373">pdf</a>, <a href="https://arxiv.org/format/2103.01373">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 Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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="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/10.1093/mnras/stab1677">10.1093/mnras/stab1677 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">A. 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Kafkes%2C+D">D. Kafkes</a>, <a href="/search/cs?searchtype=author&query=Downey%2C+K">K. Downey</a>, <a href="/search/cs?searchtype=author&query=Jenkins%2C+S">S. Jenkins</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">G. N. Perdue</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">S. Madireddy</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+T">T. Johnston</a>, <a href="/search/cs?searchtype=author&query=Snyder%2C+G+F">G. F. Snyder</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</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="2103.01373v1-abstract-short" style="display: inline;"> In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.01373v1-abstract-full').style.display = 'inline'; document.getElementById('2103.01373v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.01373v1-abstract-full" style="display: none;"> In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)$-$ and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ${\sim}20\%$. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.01373v1-abstract-full').style.display = 'none'; document.getElementById('2103.01373v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to MNRAS; 21 pages, 9 figures, 9 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-21-072-SCD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> MNRAS, Volume 506, Issue 1, September 2021, Page 677 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.13123">arXiv:2102.13123</a> <span> [<a href="https://arxiv.org/pdf/2102.13123">pdf</a>, <a href="https://arxiv.org/format/2102.13123">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</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="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/10.1093/mnras/stab2229">10.1093/mnras/stab2229 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lin%2C+Z">Zhen Lin</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+N">Nicholas Huang</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">Camille Avestruz</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+W+L+K">W. L. Kimmy Wu</a>, <a href="/search/cs?searchtype=author&query=Trivedi%2C+S">Shubhendu Trivedi</a>, <a href="/search/cs?searchtype=author&query=Caldeira%2C+J">Jo茫o Caldeira</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</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="2102.13123v2-abstract-short" style="display: inline;"> Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN). We further implement and show results for a `combined' identifier. We apply th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.13123v2-abstract-full').style.display = 'inline'; document.getElementById('2102.13123v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.13123v2-abstract-full" style="display: none;"> Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN). We further implement and show results for a `combined' identifier. We apply the methods to simulated millimeter maps for several observing frequencies for an SPT-3G-like survey. There are some key differences between the methods. The MF method requires image pre-processing to remove point sources and a model for the noise, while the CNN method requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes as input, cutout images of the sky. Specifically, we use the CNN to classify whether or not an 8 arcmin $\times$ 8 arcmin cutout of the sky contains a cluster. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some of which have SNR below the MF detection threshold. However, the CNN tends to mis-classify cutouts whose clusters are located near the edge of the cutout, which can be mitigated with staggered cutouts. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness of the MF alone are both 0.61, assuming a standard detection threshold. The purity and completeness of the CNN alone are 0.59 and 0.61. The combined classification method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many lower signal-to-noise clusters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.13123v2-abstract-full').style.display = 'none'; document.getElementById('2102.13123v2-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 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-21-077-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.10577">arXiv:2011.10577</a> <span> [<a href="https://arxiv.org/pdf/2011.10577">pdf</a>, <a href="https://arxiv.org/format/2011.10577">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</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/10.1103/PhysRevD.109.063524">10.1103/PhysRevD.109.063524 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep learning insights into cosmological structure formation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lucie-Smith%2C+L">Luisa Lucie-Smith</a>, <a href="/search/cs?searchtype=author&query=Peiris%2C+H+V">Hiranya V. Peiris</a>, <a href="/search/cs?searchtype=author&query=Pontzen%2C+A">Andrew Pontzen</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Thiyagalingam%2C+J">Jeyan Thiyagalingam</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="2011.10577v3-abstract-short" style="display: inline;"> The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.10577v3-abstract-full').style.display = 'inline'; document.getElementById('2011.10577v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.10577v3-abstract-full" style="display: none;"> The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos remains a long-standing puzzle. Here, we build a deep learning framework to investigate this question. We train a three-dimensional convolutional neural network (CNN) to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass. However, the overall scatter in the final mass predictions does not change qualitatively with this additional information, only decreasing from 0.9 dex to 0.7 dex. Given such a small improvement, our results demonstrate that isotropic aspects of the initial density field essentially saturate the relevant information about final halo mass. Therefore, instead of searching for information directly encoded in initial conditions anisotropies, a more promising route to accurate, fast halo mass predictions is to add approximate dynamical information based e.g. on perturbation theory. More broadly, our results indicate that deep learning frameworks can provide a powerful tool for extracting physical insight into cosmological structure formation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.10577v3-abstract-full').style.display = 'none'; document.getElementById('2011.10577v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 10 figures. Accepted in PRD</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. D 109, 063524 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.03591">arXiv:2011.03591</a> <span> [<a href="https://arxiv.org/pdf/2011.03591">pdf</a>, <a href="https://arxiv.org/format/2011.03591">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 Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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> <p class="title is-5 mathjax"> Domain adaptation techniques for improved cross-domain study of galaxy mergers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">A. 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Kafkes%2C+D">D. Kafkes</a>, <a href="/search/cs?searchtype=author&query=Jenkins%2C+S">S. Jenkins</a>, <a href="/search/cs?searchtype=author&query=Downey%2C+K">K. Downey</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">G. N. Perdue</a>, <a href="/search/cs?searchtype=author&query=Madireddy%2C+S">S. Madireddy</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+T">T. Johnston</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</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="2011.03591v3-abstract-short" style="display: inline;"> In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques. Here… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.03591v3-abstract-full').style.display = 'inline'; document.getElementById('2011.03591v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.03591v3-abstract-full" style="display: none;"> In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on images from one domain does not guarantee satisfactory performance on new images from a different domain. The ability to share cross-domain knowledge is the main advantage of modern deep domain adaptation techniques. Here we demonstrate the use of two techniques - Maximum Mean Discrepancy (MMD) and adversarial training with Domain Adversarial Neural Networks (DANN) - for the classification of distant galaxy mergers from the Illustris-1 simulation, where the two domains presented differ only due to inclusion of observational noise. We show how the addition of either MMD or adversarial training greatly improves the performance of the classifier on the target domain when compared to conventional machine learning algorithms, thereby demonstrating great promise for their use in astronomy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.03591v3-abstract-full').style.display = 'none'; document.getElementById('2011.03591v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Accepted in: Machine Learning and the Physical Sciences - Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS); final version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-20-582-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.11981">arXiv:2004.11981</a> <span> [<a href="https://arxiv.org/pdf/2004.11981">pdf</a>, <a href="https://arxiv.org/format/2004.11981">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</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"> DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=%C4%86iprijanovi%C4%87%2C+A">A. 膯iprijanovi膰</a>, <a href="/search/cs?searchtype=author&query=Snyder%2C+G+F">G. F. Snyder</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</a>, <a href="/search/cs?searchtype=author&query=Peek%2C+J+E+G">J. E. G. Peek</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="2004.11981v1-abstract-short" style="display: inline;"> We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the H… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.11981v1-abstract-full').style.display = 'inline'; document.getElementById('2004.11981v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.11981v1-abstract-full" style="display: none;"> We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.11981v1-abstract-full').style.display = 'none'; document.getElementById('2004.11981v1-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 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 8 figures, submitted to Astronomy & Computing</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.10710">arXiv:2004.10710</a> <span> [<a href="https://arxiv.org/pdf/2004.10710">pdf</a>, <a href="https://arxiv.org/format/2004.10710">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/2632-2153/aba6f3">10.1088/2632-2153/aba6f3 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Caldeira%2C+J">Jo茫o Caldeira</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</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="2004.10710v3-abstract-short" style="display: inline;"> We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods - Bayesian Neural Networks (BNN), Concrete Dropout (CD), and Deep Ensembles (DE) - are compared to the standard analytic error propagation. We discuss this comparison in terms endemic to both machi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.10710v3-abstract-full').style.display = 'inline'; document.getElementById('2004.10710v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.10710v3-abstract-full" style="display: none;"> We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods - Bayesian Neural Networks (BNN), Concrete Dropout (CD), and Deep Ensembles (DE) - are compared to the standard analytic error propagation. We discuss this comparison in terms endemic to both machine learning ("epistemic" and "aleatoric") and the physical sciences ("statistical" and "systematic"). The comparisons are presented in terms of simulated experimental measurements of a single pendulum - a prototypical physical system for studying measurement and analysis techniques. Our results highlight some pitfalls that may occur when using these UQ methods. For example, when the variation of noise in the training set is small, all methods predicted the same relative uncertainty independently of the inputs. This issue is particularly hard to avoid in BNN. On the other hand, when the test set contains samples far from the training distribution, we found that no methods sufficiently increased the uncertainties associated to their predictions. This problem was particularly clear for CD. In light of these results, we make some recommendations for usage and interpretation of UQ methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.10710v3-abstract-full').style.display = 'none'; document.getElementById('2004.10710v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">11 pages, 3 figures; Presented at ICLR 2020 Workshop on Fundamental Science in the era of AI; changes to match accepted version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-20-157-SCD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Mach. Learn.: Sci. Technol. 2 015002 (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.06259">arXiv:1911.06259</a> <span> [<a href="https://arxiv.org/pdf/1911.06259">pdf</a>, <a href="https://arxiv.org/format/1911.06259">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</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"> Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Caldeira%2C+J">Jo茫o Caldeira</a>, <a href="/search/cs?searchtype=author&query=Job%2C+J">Joshua Job</a>, <a href="/search/cs?searchtype=author&query=Adachi%2C+S+H">Steven H. Adachi</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">Gabriel N. Perdue</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="1911.06259v2-abstract-short" style="display: inline;"> We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems. Morphological analysis of galaxies provides critical information for studying their formation and evolution across cosmic time scales. We compress galaxy images using principal component analysis to fit a representation on the quantum… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06259v2-abstract-full').style.display = 'inline'; document.getElementById('1911.06259v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.06259v2-abstract-full" style="display: none;"> We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems. Morphological analysis of galaxies provides critical information for studying their formation and evolution across cosmic time scales. We compress galaxy images using principal component analysis to fit a representation on the quantum hardware. Then, we train RBMs with discriminative and generative algorithms, including contrastive divergence and hybrid generative-discriminative approaches, to classify different galaxy morphologies. The methods we compare include Quantum Annealing (QA), Markov Chain Monte Carlo (MCMC) Gibbs Sampling, and Simulated Annealing (SA) as well as machine learning algorithms like gradient boosted decision trees. We find that RBMs implemented on D-Wave hardware perform well, and that they show some classification performance advantages on small datasets, but they don't offer a broadly strategic advantage for this task. During this exploration, we analyzed the steps required for Boltzmann sampling with the D-Wave 2000Q, including a study of temperature estimation, and examined the impact of qubit noise by comparing and contrasting the original D-Wave 2000Q to the lower-noise version recently made available. While these analyses ultimately had minimal impact on the performance of the RBMs, we include them for reference. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06259v2-abstract-full').style.display = 'none'; document.getElementById('1911.06259v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 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">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages; LaTeX; 14 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-19-546-QIS-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.05796">arXiv:1911.05796</a> <span> [<a href="https://arxiv.org/pdf/1911.05796">pdf</a>, <a href="https://arxiv.org/ps/1911.05796">ps</a>, <a href="https://arxiv.org/format/1911.05796">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 Methods for Astrophysics">astro-ph.IM</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="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan" </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Amundson%2C+J">J. Amundson</a>, <a href="/search/cs?searchtype=author&query=Annis%2C+J">J. Annis</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">C. Avestruz</a>, <a href="/search/cs?searchtype=author&query=Bowring%2C+D">D. Bowring</a>, <a href="/search/cs?searchtype=author&query=Caldeira%2C+J">J. Caldeira</a>, <a href="/search/cs?searchtype=author&query=Cerati%2C+G">G. Cerati</a>, <a href="/search/cs?searchtype=author&query=Chang%2C+C">C. Chang</a>, <a href="/search/cs?searchtype=author&query=Dodelson%2C+S">S. Dodelson</a>, <a href="/search/cs?searchtype=author&query=Elvira%2C+D">D. Elvira</a>, <a href="/search/cs?searchtype=author&query=Farahi%2C+A">A. Farahi</a>, <a href="/search/cs?searchtype=author&query=Genser%2C+K">K. Genser</a>, <a href="/search/cs?searchtype=author&query=Gray%2C+L">L. Gray</a>, <a href="/search/cs?searchtype=author&query=Gutsche%2C+O">O. Gutsche</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">P. Harris</a>, <a href="/search/cs?searchtype=author&query=Kinney%2C+J">J. Kinney</a>, <a href="/search/cs?searchtype=author&query=Kowalkowski%2C+J+B">J. B. Kowalkowski</a>, <a href="/search/cs?searchtype=author&query=Kutschke%2C+R">R. Kutschke</a>, <a href="/search/cs?searchtype=author&query=Mrenna%2C+S">S. Mrenna</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">B. Nord</a>, <a href="/search/cs?searchtype=author&query=Para%2C+A">A. Para</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">K. Pedro</a>, <a href="/search/cs?searchtype=author&query=Perdue%2C+G+N">G. N. Perdue</a>, <a href="/search/cs?searchtype=author&query=Scheinker%2C+A">A. Scheinker</a>, <a href="/search/cs?searchtype=author&query=Spentzouris%2C+P">P. Spentzouris</a>, <a href="/search/cs?searchtype=author&query=John%2C+J+S">J. St. John</a> , et al. (5 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.05796v1-abstract-short" style="display: inline;"> We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.05796v1-abstract-full').style.display = 'inline'; document.getElementById('1911.05796v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.05796v1-abstract-full" style="display: none;"> We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.05796v1-abstract-full').style.display = 'none'; document.getElementById('1911.05796v1-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">Report number:</span> FERMILAB-FN-1092-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.02479">arXiv:1911.02479</a> <span> [<a href="https://arxiv.org/pdf/1911.02479">pdf</a>, <a href="https://arxiv.org/ps/1911.02479">ps</a>, <a href="https://arxiv.org/format/1911.02479">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 Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Connolly%2C+A+J">Andrew J. Connolly</a>, <a href="/search/cs?searchtype=author&query=Kinney%2C+J">Jamie Kinney</a>, <a href="/search/cs?searchtype=author&query=Kubica%2C+J">Jeremy Kubica</a>, <a href="/search/cs?searchtype=author&query=Narayan%2C+G">Gautaum Narayan</a>, <a href="/search/cs?searchtype=author&query=Peek%2C+J+E+G">Joshua E. G. Peek</a>, <a href="/search/cs?searchtype=author&query=Schafer%2C+C">Chad Schafer</a>, <a href="/search/cs?searchtype=author&query=Tollerud%2C+E+J">Erik J. Tollerud</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">Camille Avestruz</a>, <a href="/search/cs?searchtype=author&query=Babu%2C+G+J">G. Jogesh Babu</a>, <a href="/search/cs?searchtype=author&query=Birrer%2C+S">Simon Birrer</a>, <a href="/search/cs?searchtype=author&query=Burke%2C+D">Douglas Burke</a>, <a href="/search/cs?searchtype=author&query=Caldeira%2C+J">Jo茫o Caldeira</a>, <a href="/search/cs?searchtype=author&query=Caldwell%2C+D+A">Douglas A. Caldwell</a>, <a href="/search/cs?searchtype=author&query=Carlberg%2C+J+K">Joleen K. Carlberg</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yen-Chi Chen</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+C">Chuanfei Dong</a>, <a href="/search/cs?searchtype=author&query=Feigelson%2C+E+D">Eric D. Feigelson</a>, <a href="/search/cs?searchtype=author&query=Golkhou%2C+V+Z">V. Zach Golkhou</a>, <a href="/search/cs?searchtype=author&query=Kashyap%2C+V">Vinay Kashyap</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T+S">T. S. Li</a>, <a href="/search/cs?searchtype=author&query=Loredo%2C+T">Thomas Loredo</a>, <a href="/search/cs?searchtype=author&query=Lucie-Smith%2C+L">Luisa Lucie-Smith</a>, <a href="/search/cs?searchtype=author&query=Mandel%2C+K+S">Kaisey S. Mandel</a>, <a href="/search/cs?searchtype=author&query=Mart%C3%ADnez-Galarza%2C+J+R">J. R. Mart铆nez-Galarza</a> , et al. (13 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.02479v1-abstract-short" style="display: inline;"> The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.02479v1-abstract-full').style.display = 'inline'; document.getElementById('1911.02479v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.02479v1-abstract-full" style="display: none;"> The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.02479v1-abstract-full').style.display = 'none'; document.getElementById('1911.02479v1-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">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: substantial text overlap with arXiv:1905.05116</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-FN-1093-A-AE-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.01483">arXiv:1810.01483</a> <span> [<a href="https://arxiv.org/pdf/1810.01483">pdf</a>, <a href="https://arxiv.org/format/1810.01483">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</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="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.ascom.2019.100307">10.1016/j.ascom.2019.100307 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Caldeira%2C+J">Jo茫o Caldeira</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+W+L+K">W. L. Kimmy Wu</a>, <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">Camille Avestruz</a>, <a href="/search/cs?searchtype=author&query=Trivedi%2C+S">Shubhendu Trivedi</a>, <a href="/search/cs?searchtype=author&query=Story%2C+K+T">Kyle T. Story</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="1810.01483v3-abstract-short" style="display: inline;"> Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the C… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.01483v3-abstract-full').style.display = 'inline'; document.getElementById('1810.01483v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.01483v3-abstract-full" style="display: none;"> Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.01483v3-abstract-full').style.display = 'none'; document.getElementById('1810.01483v3-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 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages; LaTeX; 12 figures; changes to match published version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-18-515-A-CD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Astronomy and Computing 28 100307 (2019) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 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