Laurence Perreault-Levasseur

Assistant Professor

Université de Montréal

Canada

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Laurence Perreault-Levasseur is an Assistant Professor at Université de Montréal, Canada. His research focuses on the robustness of statistical methods in astrophysics, particularly in the contexts of strong gravitational lensing and cosmological simulations. Recent publications include works on neural networks for galaxy identification, parameter estimation in non-Gaussian noise, and methodologies for addressing distributional shifts in data-driven models.

Articles (11)

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2025

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2025

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2025

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2025

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2025

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2025

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2024

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2023

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2023

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2023

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2021

Collaborators (2)

Greg Bryan

Professor

Columbia University

UNITED STATES

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Maximiliano Isi Banales

Assistant Professor

Columbia University in the City of New York

UNITED STATES

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Laurence Perreault-Levasseur

Research Interests

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About

Articles (11)

Gravitational-wave Parameter Estimation in Non-Gaussian Noise Using Score-based Likelihood Characterization

Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-level Dark Matter Analysis in Strong Gravitational Lensing

Field-level Comparison and Robustness Analysis of Cosmological N-body Simulations

Using Neural Networks to Automate the Identification of Brightest Cluster Galaxies in Large Surveys

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise

A Framework for Obtaining Accurate Posteriors of Strong Gravitational Lensing Parameters with Flexible Priors and Implicit Likelihoods Using Density Estimation

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines

HInet: Generating Neutral Hydrogen from Dark Matter with Neural Networks

Collaborators (2)

Greg Bryan

Maximiliano Isi Banales

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