Justine Zeghal

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

LSST Dark Energy Science Collaboration

Eric Aubourg

Camille Avestruz

Matthew R. Becker

Biswajit Biswas

Rahul Biswas

Boris Bolliet

Adam S. Bolton

Clecio R. Bom

Raphaël Bonnet-Guerrini

Alexandre Boucaud

Jean-Eric Campagne

Chihway Chang

Aleksandra Ćiprijanović

Johann Cohen-Tanugi

Michael W. Coughlin

John Franklin Crenshaw

Juan C. Cuevas-Tello

Juan de Vicente

Seth W. Digel … (see 46 more)

Steven Dillmann

Mariano Javier de León Dominguez Romero

Alex Drlica-Wagner

Sydney Erickson

Alexander T. Gagliano

Christos Georgiou

Aritra Ghosh

Matthew Grayling

Kirill A. Grishin

Alan Heavens

Lindsay R. House

Mustapha Ishak

Wassim Kabalan

Arun Kannawadi

François Lanusse

C. Danielle Leonard

Pierre-François Léget

Michelle Lochner

Yao-Yuan Mao

Peter Melchior

Grant Merz

Martin Millon

Anais Möller

Gautham Narayan

Yuuki Omori

Hiranya Peiris

Laurence Perreault-Levasseur

Andrés A. Plazas Malagón

Nesar Ramachandra

Benjamin Remy

Cécile Roucelle

Jaime Ruiz-Zapatero

Stefan Schuldt

Ignacio Sevilla-Noarbe

Ved G. Shah

Tjitske Starkenburg

Stephen Thorp

Laura Toribio San Cipriano

Tilman Tröster

Roberto Trotta

Padma Venkatraman

Amanda Wasserman

Tim White

Justine Zeghal

Tianqing Zhang

Yuanyuan Zhang

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data… (see more) (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.

2026-01-19

arXiv (preprint)

doi.org

arxiv.org

MIRA: A Score for Conditional Distribution Accuracy and Model Comparison

Sammy Sharief

Justine Zeghal

Gabriel Missael Barco

Pablo Lemos

Yashar Hezaveh

Laurence Perreault-Levasseur

We present Mira, a method for estimating the expected probability that samples from a candidate conditional distribution match the true, unk… (see more)nown conditional distribution, for which only data-label pairs are available. We derive theoretical bounds obtained when the candidate distribution matches the true one and when the conditional distributions are independent. This framework thus enables model comparison by quantifying the alignment between the conditional distribution of a candidate model and the data-label pairs of the true model. Consequently, Mira enables Bayesian model comparison through direct posterior validation, bypassing the challenging evidence computation. We demonstrate its effectiveness across several toy problems and Bayesian inference tasks.

2025-12-31

International Conference on Machine Learning (Accept (spotlight))

doi.org

openreview.net

Bridging Simulators with Conditional Optimal Transport

Justine Zeghal

Benjamin Remy

Yashar Hezaveh

François Lanusse

Laurence Perreault-Levasseur

2025-10-27

ArXiv (preprint)