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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.02857">arXiv:2410.02857</a> <span> [<a href="https://arxiv.org/pdf/2410.02857">pdf</a>, <a href="https://arxiv.org/format/2410.02857">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hsu%2C+A">Alan Hsu</a>, <a href="/search/cs?searchtype=author&query=Ho%2C+M">Matthew Ho</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+J">Joyce Lin</a>, <a href="/search/cs?searchtype=author&query=Markey%2C+C">Carleen Markey</a>, <a href="/search/cs?searchtype=author&query=Ntampaka%2C+M">Michelle Ntampaka</a>, <a href="/search/cs?searchtype=author&query=Trac%2C+H">Hy Trac</a>, <a href="/search/cs?searchtype=author&query=P%C3%B3czos%2C+B">Barnab谩s P贸czos</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="2410.02857v1-abstract-short" style="display: inline;"> We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional observations, and generates realizations of corresponding gas and dark matter maps by sampling from a learned data posterior. We train and validate the performance of our model by using m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02857v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02857v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02857v1-abstract-full" style="display: none;"> We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional observations, and generates realizations of corresponding gas and dark matter maps by sampling from a learned data posterior. We train and validate the performance of our model by using mock data from a hydrodynamical cosmological simulation. The model accurately reconstructs both the mean and spread of the radial density profiles in the spatial domain to within 5\%, indicating that the model is able to distinguish between clusters of different sizes. In the spectral domain, the model achieves close-to-unity values for the bias and cross-correlation coefficients, indicating that the model can accurately probe cluster structures on both large and small scales. Our experiments demonstrate the ability of score models to learn a strong, nonlinear, and unbiased mapping between input observables and fundamental density distributions of galaxy clusters. These diffusion models can be further fine-tuned and generalized to not only take in additional observables as inputs, but also real observations and predict unknown density distributions of galaxy clusters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02857v1-abstract-full').style.display = 'none'; document.getElementById('2410.02857v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 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">15 pages, 9 figures, submitted to The Open Journal of Astrophysics</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.20389">arXiv:2405.20389</a> <span> [<a href="https://arxiv.org/pdf/2405.20389">pdf</a>, <a href="https://arxiv.org/format/2405.20389">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="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Designing an Evaluation Framework for Large Language Models in Astronomy Research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+J+F">John F. Wu</a>, <a href="/search/cs?searchtype=author&query=Hyk%2C+A">Alina Hyk</a>, <a href="/search/cs?searchtype=author&query=McCormick%2C+K">Kiera McCormick</a>, <a href="/search/cs?searchtype=author&query=Ye%2C+C">Christine Ye</a>, <a href="/search/cs?searchtype=author&query=Astarita%2C+S">Simone Astarita</a>, <a href="/search/cs?searchtype=author&query=Baral%2C+E">Elina Baral</a>, <a href="/search/cs?searchtype=author&query=Ciuca%2C+J">Jo Ciuca</a>, <a href="/search/cs?searchtype=author&query=Cranney%2C+J">Jesse Cranney</a>, <a href="/search/cs?searchtype=author&query=Field%2C+A">Anjalie Field</a>, <a href="/search/cs?searchtype=author&query=Iyer%2C+K">Kartheik Iyer</a>, <a href="/search/cs?searchtype=author&query=Koehn%2C+P">Philipp Koehn</a>, <a href="/search/cs?searchtype=author&query=Kotler%2C+J">Jenn Kotler</a>, <a href="/search/cs?searchtype=author&query=Kruk%2C+S">Sandor Kruk</a>, <a href="/search/cs?searchtype=author&query=Ntampaka%2C+M">Michelle Ntampaka</a>, <a href="/search/cs?searchtype=author&query=O%27Neill%2C+C">Charles O'Neill</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=Sharma%2C+S">Sanjib Sharma</a>, <a href="/search/cs?searchtype=author&query=Yunus%2C+M">Mikaeel Yunus</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.20389v1-abstract-short" style="display: inline;"> Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currently no standard for evaluating the use of LLMs in astronomy. Therefore, we present the experimental design for an evaluation study on how astronomy rese… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20389v1-abstract-full').style.display = 'inline'; document.getElementById('2405.20389v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20389v1-abstract-full" style="display: none;"> Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currently no standard for evaluating the use of LLMs in astronomy. Therefore, we present the experimental design for an evaluation study on how astronomy researchers interact with LLMs. We deploy a Slack chatbot that can answer queries from users via Retrieval-Augmented Generation (RAG); these responses are grounded in astronomy papers from arXiv. We record and anonymize user questions and chatbot answers, user upvotes and downvotes to LLM responses, user feedback to the LLM, and retrieved documents and similarity scores with the query. Our data collection method will enable future dynamic evaluations of LLM tools for astronomy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20389v1-abstract-full').style.display = 'none'; document.getElementById('2405.20389v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 3 figures. Code available at https://github.com/jsalt2024-evaluating-llms-for-astronomy/astro-arxiv-bot</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> </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> 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