Portrait of Laurence Perreault-Levasseur is unavailable

Laurence Perreault-Levasseur

Associate Academic Member

Assistant Professor, Université de Montréal, Department of Physics

Research Topics

Computer Vision

Deep Learning

Dynamical Systems

Generative Models

Graph Neural Networks

Probabilistic Models

Biography

Laurence Perreault-Levasseur is the Canada Research Chair in Computational Cosmology and Artificial Intelligence. She is an assistant professor at Université de Montréal and an associate academic member of Mila – Quebec Artificial Intelligence Institute. Perreault-Levasseur’s research focuses on the development and application of machine learning methods to cosmology.

She is also a Visiting Scholar at the Flatiron Institute in New York City. Prior to that, she was a research fellow at their Center for Computational Astrophysics, and a KIPAC postdoctoral fellow at Stanford University.

For her PhD degree at the University of Cambridge, she worked on applications of open effective field theory methods to the formalism of inflation. She completed her BSc and MSc degrees at McGill University.

Current Students

PhD - Université de Montréal

Tara Akhound-Sadegh

PhD - McGill University

Principal supervisor :

Siamak Ravanbakhsh

eirini angeloudi

Research Intern - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

andreas.filipp@umontreal.ca

Patrick Janulewicz

Master's Research - McGill University

Master's Research - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Michael Matesic

PhD - Université de Montréal

Master's Research - Université de Montréal

Principal supervisor :

Sacha Perry-Fagant

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

Sammy Nasser Sharief

Master's Research - Université de Montréal

Postdoctorate - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Postdoctorate - McGill University

Co-supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Publications

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

Laurence Perreault-Levasseur

2024-11-22

Journal of Open Source Software (published)

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

Andreas Filipp

Laurence Perreault-Levasseur

2024-11-08

ArXiv (preprint)

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization

Ronan Legin

Maximiliano Isi

Kaze W. K. Wong

Laurence Perreault-Levasseur

2024-10-25

ArXiv (preprint)

A Data-driven Discovery of the Causal Connection between Galaxy and Black Hole Evolution

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Tristan Deleu

Yu Luo

Changhyun Cho

Pablo Lemos

Laurence Perreault-Levasseur

Xi Kang

Andrea Maccio

2024-10-01

ArXiv (preprint)

Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine

C. Rhea

Julie Hlavacek-larrondo

Alexandre Adam

Ralph P. Kraft

Ákos Bogdán

Laurence Perreault-Levasseur

Marine Prunier

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new fr… (see more)ontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-

2024-09-16

ArXiv (preprint)

Strong gravitational lensing as a probe of dark matter

Simona Vegetti

Simon Birrer

Giulia Despali

C. Fassnacht

Daniel A. Gilman

L.

Laurence Perreault-Levasseur

J. McKean

D. Powell

Conor M. O'riordan

G.

Vernardos

Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple… (see more) images of lensed sources. Strong gravitational lensing provides, therefore, a key test of different dark matter models in a way that is independent of the baryonic content of matter structures on subgalactic scales. In this chapter, we describe how galaxy-scale strong gravitational lensing observations are sensitive to the physical nature of dark matter. We provide a historical perspective of the field, and review its current status. We discuss the challenges and advances in terms of data, treatment of systematic errors and theoretical predictions, that will enable one to deliver a stringent and robust test of different dark matter models in the near future. With the advent of the next generation of sky surveys, the number of known strong gravitational lens systems is expected to increase by several orders of magnitude. Coupled with high-resolution follow-up observations, these data will provide a key opportunity to constrain the properties of dark matter with strong gravitational lensing.

2024-07-30

Space Science Reviews (published)

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

Gabriel Missael Barco

Alexandre Adam

Connor Stone

Laurence Perreault-Levasseur

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-… (see more)level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations and showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates.

2024-07-24

ArXiv (preprint)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Pablo Lemos

Nikolay Malkin

Will Handley

Laurence Perreault-Levasseur

We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable … (see more)programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows GGNS to scale well with dimensionality and perform competitively on a variety of synthetic and real-world problems. We also show the potential of combining nested sampling with generative flow networks to obtain large amounts of high-quality samples from the posterior distribution. This combination leads to faster mode discovery and more accurate estimates of the partition function.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

Assessing the Viability of Generative Modeling in Simulated Astronomical Observations

Patrick Janulewicz

Laurence Perreault-Levasseur

Tracy Webb

In this paper, we use methods for assessing the quality of generative models and apply them to a problem from the physical sciences. We turn… (see more) our attention to astrophysics, where cosmological simulations are often used to create mock observations that mimic telescope images. These simulations and their mock observations are often slow and challenging to generate, inspiring some to use generative modeling to enhance the amount of data available to study. In this work, we add realism to simulated images of galaxy clusters and use probability mass estimation to assess their fidelity compared to reality. We find that the simulations are biased compared to real observations and suggest that researchers applying generative modeling to these systems should proceed with caution.

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing

Andreas Filipp

Laurence Perreault-Levasseur

In recent years, there has been increasing interest in the field of astrophysics in applying Neural Ratio Estimators (NREs) to large-scale i… (see more)nference problems where both amortization and marginalization over a large number of nuisance parameters are needed. Here, in order to assess the true potential of this method to produce unbiased inference on real data, we investigate the robustness of NREs to distribution shifts and model misspecification in the specific scientific application of the measurement of dark matter population-level parameters using strong gravitational lensing. We investigate the behaviour of a trained NRE for test data presenting distributional shifts inside the bounds of training, as well as out of distribution, both in the linear and non-linear parameters of this problem. While our results show that NREs perform when tested perfectly in distribution, we find that they exhibit significant biases and drawbacks when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs to real astrophysical data, where underlying distributions are not perfectly known and models do not perfectly reconstruct the true underlying distributions.

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics

Antoine Bourdin

Ronan Legin

Matthew Ho

Alexandre Adam

Laurence Perreault-Levasseur

2024-06-16

ICML.cc/2024/Workshop/AI4Science (poster)

Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at $z\sim6.6$

Manuela Bischetti

Hyunseop Choi

Fabrizio Fiore

Chiara Feruglio

Stefano Carniani

Valentina D'Odorico

Eduardo Banados

Huanqing Chen

Roberto Decarli

Simona Gallerani

Julie Hlavacek-larrondo

Samuel Lai

K. Leighly

Chiara Mazzucchelli

Laurence Perreault-Levasseur

Roberta Tripodi

Fabian Walter

Feige Wang

Jinyi Yang

Maria Vittoria Zanchettin … (see 1 more)

Yongda Zhu

2024-04-18

ArXiv (preprint)