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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Buck, T"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08557">arXiv:2411.08557</a> <span> [<a href="https://arxiv.org/pdf/2411.08557">pdf</a>, <a href="https://arxiv.org/format/2411.08557">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> </div> </div> <p class="title is-5 mathjax"> Learning Locally Adaptive Metrics that Enhance Structural Representation with $\texttt{LAMINAR}$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kleiber%2C+C">Christian Kleiber</a>, <a href="/search/cs?searchtype=author&query=Oliver%2C+W+H">William H. Oliver</a>, <a href="/search/cs?searchtype=author&query=Buck%2C+T">Tobias Buck</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="2411.08557v1-abstract-short" style="display: inline;"> We present $\texttt{LAMINAR}$, a novel unsupervised machine learning pipeline designed to enhance the representation of structure within data via producing a more-informative distance metric. Analysis methods in the physical sciences often rely on standard metrics to define geometric relationships in data, which may fail to capture the underlying structure of complex data sets. $\texttt{LAMINAR}$… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08557v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08557v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08557v1-abstract-full" style="display: none;"> We present $\texttt{LAMINAR}$, a novel unsupervised machine learning pipeline designed to enhance the representation of structure within data via producing a more-informative distance metric. Analysis methods in the physical sciences often rely on standard metrics to define geometric relationships in data, which may fail to capture the underlying structure of complex data sets. $\texttt{LAMINAR}$ addresses this by using a continuous-normalising-flow and inverse-transform-sampling to define a Riemannian manifold in the data space without the need for the user to specify a metric over the data a-priori. The result is a locally-adaptive-metric that produces structurally-informative density-based distances. We demonstrate the utility of $\texttt{LAMINAR}$ by comparing its output to the Euclidean metric for structured data sets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08557v1-abstract-full').style.display = 'none'; document.getElementById('2411.08557v1-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 NeurIPS 2024 Machine Learning and the Physical Sciences workshop. 6 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20886">arXiv:2410.20886</a> <span> [<a href="https://arxiv.org/pdf/2410.20886">pdf</a>, <a href="https://arxiv.org/format/2410.20886">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="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> CODES: Benchmarking Coupled ODE Surrogates </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Janssen%2C+R">Robin Janssen</a>, <a href="/search/cs?searchtype=author&query=Sulzer%2C+I">Immanuel Sulzer</a>, <a href="/search/cs?searchtype=author&query=Buck%2C+T">Tobias Buck</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="2410.20886v2-abstract-short" style="display: inline;"> We introduce CODES, a benchmark for comprehensive evaluation of surrogate architectures for coupled ODE systems. Besides standard metrics like mean squared error (MSE) and inference time, CODES provides insights into surrogate behaviour across multiple dimensions like interpolation, extrapolation, sparse data, uncertainty quantification and gradient correlation. The benchmark emphasizes usability… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20886v2-abstract-full').style.display = 'inline'; document.getElementById('2410.20886v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20886v2-abstract-full" style="display: none;"> We introduce CODES, a benchmark for comprehensive evaluation of surrogate architectures for coupled ODE systems. Besides standard metrics like mean squared error (MSE) and inference time, CODES provides insights into surrogate behaviour across multiple dimensions like interpolation, extrapolation, sparse data, uncertainty quantification and gradient correlation. The benchmark emphasizes usability through features such as integrated parallel training, a web-based configuration generator, and pre-implemented baseline models and datasets. Extensive documentation ensures sustainability and provides the foundation for collaborative improvement. By offering a fair and multi-faceted comparison, CODES helps researchers select the most suitable surrogate for their specific dataset and application while deepening our understanding of surrogate learning behaviour. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20886v2-abstract-full').style.display = 'none'; document.getElementById('2410.20886v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 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">13 pages, 10 figures, accepted for the Machine Learning and the Physical Sciences workshop at NeurIPS 2024, source code available on GitHub at https://github.com/robin-janssen/CODES-Benchmark</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.06015">arXiv:2312.06015</a> <span> [<a href="https://arxiv.org/pdf/2312.06015">pdf</a>, <a href="https://arxiv.org/format/2312.06015">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"> Speeding up astrochemical reaction networks with autoencoders and neural ODEs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sulzer%2C+I">Immanuel Sulzer</a>, <a href="/search/cs?searchtype=author&query=Buck%2C+T">Tobias Buck</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.06015v1-abstract-short" style="display: inline;"> In astrophysics, solving complex chemical reaction networks is essential but computationally demanding due to the high dimensionality and stiffness of the ODE systems. Traditional approaches for reducing computational load are often specialized to specific chemical networks and require expert knowledge. This paper introduces a machine learning-based solution employing autoencoders for dimensionali… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06015v1-abstract-full').style.display = 'inline'; document.getElementById('2312.06015v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.06015v1-abstract-full" style="display: none;"> In astrophysics, solving complex chemical reaction networks is essential but computationally demanding due to the high dimensionality and stiffness of the ODE systems. Traditional approaches for reducing computational load are often specialized to specific chemical networks and require expert knowledge. This paper introduces a machine learning-based solution employing autoencoders for dimensionality reduction and a latent space neural ODE solver to accelerate astrochemical reaction network computations. Additionally, we propose a cost-effective latent space linear function solver as an alternative to neural ODEs. These methods are assessed on a dataset comprising 29 chemical species and 224 reactions. Our findings demonstrate that the neural ODE achieves a 55x speedup over the baseline model while maintaining significantly higher accuracy by up to two orders of magnitude reduction in relative error. Furthermore, the linear latent model enhances accuracy and achieves a speedup of up to 4000x compared to standard methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06015v1-abstract-full').style.display = 'none'; document.getElementById('2312.06015v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at the "Machine Learning and the Physical Sciences" Workshop at Neurips, 2023</span> </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> 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