LSST Dark Energy Science Collaboration
Published January 20, 2026 | Version 1.0.0
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Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

Authors/Creators

  • 1. Université Paris Cité, CNRS, CEA, Astroparticule et Cosmologie, F-75013 Paris, France
  • 2. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA; Leinweber Institute of Theoretical Physics, University of Michigan, Ann Arbor, MI 48109, USA
  • 3. Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
  • 4. Independent
  • 5. Cavendish Astrophysics, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK; Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
  • 6. SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  • 7. Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro, Brazil
  • 8. Department of Computer Science, University of Milan, Milan, Italy
  • 9. Université Paris Cité, CNRS, Astroparticule et Cosmologie, F-75013 Paris, France
  • 10. Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France
  • 11. Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA; Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA; NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA
  • 12. Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA; Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA; NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA
  • 13. Université Clermont-Auvergne, CNRS, LPCA, 63000 Clermont-Ferrand, France
  • 14. School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA
  • 15. Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305, USA; Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  • 16. Engineering Faculty, Universidad Autonoma de San Luis Potosi, Zona Universitaria, San Luis Potosi, 78290, Mexico
  • 17. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
  • 18. SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305, USA
  • 19. Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305, USA; Stanford Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  • 20. Instituto de Astronomía Teórica y Experimental (IATE - UNC and CONICET CCT Córdoba), Observatorio Astronómico de Córdoba, Universidad Nacional de Córdoba, Laprida 854, X5000BGR, Córdoba, Argentina
  • 21. Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA; Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA; Kavli Institute of Cosmological Physics, University of Chicago, Chicago, IL 60637, USA; NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA
  • 22. Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  • 23. The NSF AI Institute for Artificial Intelligence and Fundamental Interactions; Center for Astrophysics \textbar{; Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  • 24. Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain
  • 25. Department of Astronomy & DiRAC Institute, University of Washington, Seattle, WA 98195, USA
  • 26. Institute of Astronomy and Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK
  • 27. Imperial Centre for Inference and Cosmology (ICIC), Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK
  • 28. NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA; Data Science Institute, The University of Chicago, Chicago, IL 60615, USA
  • 29. Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA
  • 30. Department of Physics, Duke University, Durham, NC 27708, USA
  • 31. Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, F-91191 Gif-sur-Yvette, France
  • 32. School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
  • 33. Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA
  • 34. Department of Physics and Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
  • 35. Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA
  • 36. Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
  • 37. Department of Astronomy, University of Illinois Urbana Champaign, 1002 W. Green St., Urbana, IL, 61801, USA
  • 38. Institute for Particle Physics and Astrophysics, ETH Zürich, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland
  • 39. Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
  • 40. Department of Astronomy, University of Illinois Urbana Champaign, 1002 W. Green St., Urbana, IL, 61801, USA; NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA
  • 41. Département de Physique, Université de Montréal, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, QC, H2V 0B3, Canada; Ciela - Montréal Institute for Astrophysical Data Analysis and Machine Learning, Montréal, QC H2V 0B3, Canada; Mila - Quebec Artificial Intelligence Institute, Montréal, QC H2S 3H1, Canada
  • 42. Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
  • 43. Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA; NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA
  • 44. Advanced Research Computing Centre, University College London, 90 High Holborn, London WC1V 6LJ, UK
  • 45. Dipartimento di Fisica, Universit\`a degli Studi di Milano, via Celoria 16, I-20133 Milano, Italy; Finnish Centre for Astronomy with ESO (FINCA), University of Turku, FI-20014 Turku, Finland; Department of Physics, P.O. Box 64, University of Helsinki, FI-00014 Helsinki, Finland; INAF - IASF Milano, via A. Corti 12, I-20133 Milano, Italy
  • 46. Department of Physics and Astronomy, Northwestern University, Evanston, IL, USA; Center for Interdisciplinary Exploration and Research in Astrophysics, Northwestern University, Evanston, IL, USA; NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA
  • 47. Theoretical and Scientific Data Science, International School for Advanced Study, Via Bonomea 265, I-34136 Trieste, Italy; Imperial Centre for Inference and Cosmology (ICIC), Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK
  • 48. Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
  • 49. Département de Physique, Université de Montréal, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, QC, H2V 0B3, Canada; Mila - Quebec Artificial Intelligence Institute, Montréal, QC H2S 3H1, Canada
  • 50. Department of Physics and Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA
  • 51. NSF NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA

Description

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. Taken together, DESC's combination of community-accessible data, demanding scientific requirements, and mature simulation infrastructure makes the collaboration an excellent testbed for developing and validating robust AI/ML practices for fundamental physics.

Notes

This white paper was produced by the AI for DESC White Paper Task Force of the LSST Dark Energy Science Collaboration.

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Additional details

Identifiers

arXiv
arXiv:2601.14235

Dates

Available
2026-01-20

Software

Repository URL
https://github.com/LSSTDESC/AI_For_DESC