Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915742237065216 |
|---|---|
| author | LSST Dark Energy Science Collaboration Aubourg, Eric Avestruz, Camille Becker, Matthew R. Biswas, Biswajit Biswas, Rahul Bolliet, Boris Bolton, Adam S. Bom, Clecio R. Bonnet-Guerrini, Raphaël Boucaud, Alexandre Campagne, Jean-Eric Chang, Chihway Ćiprijanović, Aleksandra Cohen-Tanugi, Johann Coughlin, Michael W. Crenshaw, John Franklin Cuevas-Tello, Juan C. de Vicente, Juan Digel, Seth W. Dillmann, Steven Romero, Mariano Javier de León Dominguez Drlica-Wagner, Alex Erickson, Sydney Gagliano, Alexander T. Georgiou, Christos Ghosh, Aritra Grayling, Matthew Grishin, Kirill A. Heavens, Alan House, Lindsay R. Ishak, Mustapha Kabalan, Wassim Kannawadi, Arun Lanusse, François Leonard, C. Danielle Léget, Pierre-François Lochner, Michelle Mao, Yao-Yuan Melchior, Peter Merz, Grant Millon, Martin Möller, Anais Narayan, Gautham Omori, Yuuki Peiris, Hiranya Perreault-Levasseur, Laurence Malagón, Andrés A. Plazas Ramachandra, Nesar Remy, Benjamin Roucelle, Cécile Ruiz-Zapatero, Jaime Schuldt, Stefan Sevilla-Noarbe, Ignacio Shah, Ved G. Starkenburg, Tjitske Thorp, Stephen Cipriano, Laura Toribio San Tröster, Tilman Trotta, Roberto Venkatraman, Padma Wasserman, Amanda White, Tim Zeghal, Justine Zhang, Tianqing Zhang, Yuanyuan |
| author_facet | LSST Dark Energy Science Collaboration Aubourg, Eric Avestruz, Camille Becker, Matthew R. Biswas, Biswajit Biswas, Rahul Bolliet, Boris Bolton, Adam S. Bom, Clecio R. Bonnet-Guerrini, Raphaël Boucaud, Alexandre Campagne, Jean-Eric Chang, Chihway Ćiprijanović, Aleksandra Cohen-Tanugi, Johann Coughlin, Michael W. Crenshaw, John Franklin Cuevas-Tello, Juan C. de Vicente, Juan Digel, Seth W. Dillmann, Steven Romero, Mariano Javier de León Dominguez Drlica-Wagner, Alex Erickson, Sydney Gagliano, Alexander T. Georgiou, Christos Ghosh, Aritra Grayling, Matthew Grishin, Kirill A. Heavens, Alan House, Lindsay R. Ishak, Mustapha Kabalan, Wassim Kannawadi, Arun Lanusse, François Leonard, C. Danielle Léget, Pierre-François Lochner, Michelle Mao, Yao-Yuan Melchior, Peter Merz, Grant Millon, Martin Möller, Anais Narayan, Gautham Omori, Yuuki Peiris, Hiranya Perreault-Levasseur, Laurence Malagón, Andrés A. Plazas Ramachandra, Nesar Remy, Benjamin Roucelle, Cécile Ruiz-Zapatero, Jaime Schuldt, Stefan Sevilla-Noarbe, Ignacio Shah, Ved G. Starkenburg, Tjitske Thorp, Stephen Cipriano, Laura Toribio San Tröster, Tilman Trotta, Roberto Venkatraman, Padma Wasserman, Amanda White, Tim Zeghal, Justine Zhang, Tianqing Zhang, Yuanyuan |
| contents | The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14235 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration LSST Dark Energy Science Collaboration Aubourg, Eric Avestruz, Camille Becker, Matthew R. Biswas, Biswajit Biswas, Rahul Bolliet, Boris Bolton, Adam S. Bom, Clecio R. Bonnet-Guerrini, Raphaël Boucaud, Alexandre Campagne, Jean-Eric Chang, Chihway Ćiprijanović, Aleksandra Cohen-Tanugi, Johann Coughlin, Michael W. Crenshaw, John Franklin Cuevas-Tello, Juan C. de Vicente, Juan Digel, Seth W. Dillmann, Steven Romero, Mariano Javier de León Dominguez Drlica-Wagner, Alex Erickson, Sydney Gagliano, Alexander T. Georgiou, Christos Ghosh, Aritra Grayling, Matthew Grishin, Kirill A. Heavens, Alan House, Lindsay R. Ishak, Mustapha Kabalan, Wassim Kannawadi, Arun Lanusse, François Leonard, C. Danielle Léget, Pierre-François Lochner, Michelle Mao, Yao-Yuan Melchior, Peter Merz, Grant Millon, Martin Möller, Anais Narayan, Gautham Omori, Yuuki Peiris, Hiranya Perreault-Levasseur, Laurence Malagón, Andrés A. Plazas Ramachandra, Nesar Remy, Benjamin Roucelle, Cécile Ruiz-Zapatero, Jaime Schuldt, Stefan Sevilla-Noarbe, Ignacio Shah, Ved G. Starkenburg, Tjitske Thorp, Stephen Cipriano, Laura Toribio San Tröster, Tilman Trotta, Roberto Venkatraman, Padma Wasserman, Amanda White, Tim Zeghal, Justine Zhang, Tianqing Zhang, Yuanyuan Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Artificial Intelligence Machine Learning The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors. |
| title | Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2601.14235 |