Justine Zeghal

Postdoctorate - Université de Montréal

Supervisor

Laurence Perreault-Levasseur

Co-supervisor

Yashar Hezaveh

Research Topics

Bayesian Inference

Cosmology

Generative Models

Probabilistic Models

Website

Google Scholar

GitHub

Publications

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

LSST Dark Energy Science Collaboration

Eric Aubourg

Camille Avestruz

M. R. Becker

Biswajit Biswas

Rahul Biswas

Boris Bolliet

César Briceño

Clecio Bom

Raphaël Bonnet-Guerrini

Alexandre Boucaud

J.E. Campagne

Chihway Chang

Aleksandra Ćiprijanović

Johann Cohen-Tanugi

Michael W. Coughlin

John Franklin Crenshaw

Juan C. Cuevas‐Tello

Juan de Vicente

Seth William Digel … (see 50 more)

Steven Dillmann

Mariano Javier de León Dominguez Romero

Alex Drlica-Wagner

Sydney Erickson

Alexander Gagliano

Christos Georgiou

Aritra Ghosh

Matthew Grayling

Kirill A. Grishin

Alan Heavens

Lindsay R. House

Mustapha Ishak

Wassim Kabalan

Olivia Lynn

François Lanusse

C. Danielle Leonard

P.-F. Léget

Michelle Lochner

Joel Meyers

Peter Melchior

Grant Merz

Martin Millon

Anais Möller

G. Narayan

Yuuki Omori

Hiranya Peiris

Laurence Perreault-Levasseur

A. A. Plazas

Nesar Ramachandra

B. Remy

C. Roucelle

Jaime Ruiz-Zapatero

Stefan Schuldt

I. Sevilla-Noarbe

Ved G. Shah

Tjitske Starkenburg

Stephen Thorp

Tianqing Zhang

Tilman Tröster

Roberto Trotta

Padma T. Venkatraman

A. R. Wasserman

Tim White

Justine Zeghal

Tianqing Zhang

Yuanyuan Zhang

Adam S. Bolton

Arun Kannawadi

Yao-Yuan Mao

Laura Toribio San Cipriano

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

Bridging Simulators with Conditional Optimal Transport

Justine Zeghal

Benjamin Remy

Yashar Hezaveh

François Lanusse

Laurence Perreault-Levasseur

2025-10-27

ArXiv (preprint)