Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration. Collaboration, L. D. E. S., Aubourg, E., Avestruz, C., Becker, M. R., Biswas, B., Biswas, R., Bolliet, B., Bolton, A. S., Bom, C. R., Bonnet-Guerrini, R., Boucaud, A., Campagne, J., Chang, C., Ćiprijanović, A., Cohen-Tanugi, J., Coughlin, M. W., Crenshaw, J. F., Cuevas-Tello, J. C., Vicente, J. d., Digel, S. W., Dillmann, S., Romero, M. J. d. L. D., Drlica-Wagner, A., Erickson, S., Gagliano, A. T., Georgiou, C., Ghosh, A., Grayling, M., Grishin, K. A., Heavens, A., House, L. R., Ishak, M., Kabalan, W., Kannawadi, A., Lanusse, F., Leonard, C. D., Léget, P., Lochner, M., Mao, Y., Melchior, P., Merz, G., Millon, M., Möller, A., Narayan, G., Omori, Y., Peiris, H., Perreault-Levasseur, L., Malagón, A. A. P., Ramachandra, N., Remy, B., Roucelle, C., Ruiz-Zapatero, J., Schuldt, S., Sevilla-Noarbe, I., Shah, V. G., Starkenburg, T., Thorp, S., Cipriano, L. T. S., Tröster, T., Trotta, R., Venkatraman, P., Wasserman, A., White, T., Zeghal, J., Zhang, T., & Zhang, Y. Technical Report January, 2026. arXiv:2601.14235 [astro-ph]

Paper doi abstract bibtex

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.

@techreport{collaboration_opportunities_2026,
title = {Opportunities in {AI}/{ML} for the {Rubin} {LSST} {Dark} {Energy} {Science} {Collaboration}},
url = {http://arxiv.org/abs/2601.14235},
doi = {10.5281/zenodo.18319953},
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.},
language = {en},
urldate = {2026-02-06},
author = {Collaboration, LSST Dark Energy Science and Aubourg, Eric and Avestruz, Camille and Becker, Matthew R. and Biswas, Biswajit and Biswas, Rahul and Bolliet, Boris and Bolton, Adam S. and Bom, Clecio R. and Bonnet-Guerrini, Raphaël and Boucaud, Alexandre and Campagne, Jean-Eric and Chang, Chihway and Ćiprijanović, Aleksandra and Cohen-Tanugi, Johann and Coughlin, Michael W. and Crenshaw, John Franklin and Cuevas-Tello, Juan C. and Vicente, Juan de and Digel, Seth W. and Dillmann, Steven and Romero, Mariano Javier de León Dominguez and Drlica-Wagner, Alex and Erickson, Sydney and Gagliano, Alexander T. and Georgiou, Christos and Ghosh, Aritra and Grayling, Matthew and Grishin, Kirill A. and Heavens, Alan and House, Lindsay R. and Ishak, Mustapha and Kabalan, Wassim and Kannawadi, Arun and Lanusse, François and Leonard, C. Danielle and Léget, Pierre-François and Lochner, Michelle and Mao, Yao-Yuan and Melchior, Peter and Merz, Grant and Millon, Martin and Möller, Anais and Narayan, Gautham and Omori, Yuuki and Peiris, Hiranya and Perreault-Levasseur, Laurence and Malagón, Andrés A. Plazas and Ramachandra, Nesar and Remy, Benjamin and Roucelle, Cécile and Ruiz-Zapatero, Jaime and Schuldt, Stefan and Sevilla-Noarbe, Ignacio and Shah, Ved G. and Starkenburg, Tjitske and Thorp, Stephen and Cipriano, Laura Toribio San and Tröster, Tilman and Trotta, Roberto and Venkatraman, Padma and Wasserman, Amanda and White, Tim and Zeghal, Justine and Zhang, Tianqing and Zhang, Yuanyuan},
month = jan,
year = {2026},
note = {arXiv:2601.14235 [astro-ph]},
keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

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