Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
Abstract
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.
Keywords
Cite
@article{arxiv.2601.14235,
title = {Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration},
author = {LSST Dark Energy Science Collaboration and Eric Aubourg and Camille Avestruz and Matthew R. Becker and Biswajit Biswas and Rahul Biswas and Boris Bolliet and Adam S. Bolton and Clecio R. Bom and Raphaël Bonnet-Guerrini and Alexandre Boucaud and Jean-Eric Campagne and Chihway Chang and Aleksandra Ćiprijanović and Johann Cohen-Tanugi and Michael W. Coughlin and John Franklin Crenshaw and Juan C. Cuevas-Tello and Juan de Vicente and Seth W. Digel and Steven Dillmann and Mariano Javier de León Dominguez Romero and Alex Drlica-Wagner and Sydney Erickson and Alexander T. Gagliano and Christos Georgiou and Aritra Ghosh and Matthew Grayling and Kirill A. Grishin and Alan Heavens and Lindsay R. House and Mustapha Ishak and Wassim Kabalan and Arun Kannawadi and François Lanusse and C. Danielle Leonard and Pierre-François Léget and Michelle Lochner and Yao-Yuan Mao and Peter Melchior and Grant Merz and Martin Millon and Anais Möller and Gautham Narayan and Yuuki Omori and Hiranya Peiris and Laurence Perreault-Levasseur and Andrés A. Plazas Malagón and Nesar Ramachandra and Benjamin Remy and Cécile Roucelle and Jaime Ruiz-Zapatero and Stefan Schuldt and Ignacio Sevilla-Noarbe and Ved G. Shah and Tjitske Starkenburg and Stephen Thorp and Laura Toribio San Cipriano and Tilman Tröster and Roberto Trotta and Padma Venkatraman and Amanda Wasserman and Tim White and Justine Zeghal and Tianqing Zhang and Yuanyuan Zhang},
journal= {arXiv preprint arXiv:2601.14235},
year = {2026}
}
Comments
84 pages. This is v1.0 of the DESC's white paper on AI/ML, a collaboration document that is being made public but which is not planned for submission to a journal