Laurence Perreault-Levasseur

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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

Alexandre Adam

PhD - Université de Montréal

Google Scholar

Tara Akhound-Sadegh

PhD - McGill University

Principal supervisor :

Siamak Ravanbakhsh

eirini angeloudi

Research Intern - Université de Montréal

Co-supervisor :

Missael Barco

Master's Research - Université de Montréal

Co-supervisor :

Misha Barth

PhD - Université de Montréal

Principal supervisor :

Antoine Bourdin

Research Intern - McGill University

Co-supervisor :

Joseph Choi

Postdoctorate - Université de Montréal

Etienne Collin

Research Intern - Université de Montréal

Principal supervisor :

contact@etiennecollin.com

Noé Dia

Research Intern - Université de Montréal

Co-supervisor :

Andreas Filipp

PhD - Université de Montréal

Co-supervisor :

andreas.filipp@umontreal.ca

Patrick Janulewicz

Master's Research - McGill University

Rawan Karam

Master's Research - Université de Montréal

Jenna Karcheski

Research Intern - Université de Montréal

Principal supervisor :

Stephanie Lee

Research Intern - Université de Montréal

Principal supervisor :

Ronan Legin

PhD - Université de Montréal

Principal supervisor :

Pablo Lemos

Postdoctorate - Université de Montréal

Co-supervisor :

Research Intern - Université de Montréal

Co-supervisor :

Parker Levesque

Master's Research - Université de Montréal

Co-supervisor :

Michael Matesic

PhD - Université de Montréal

Mario Pasquato

Independent visiting researcher - University of Padua

Master's Research - Université de Montréal

Principal supervisor :

Sacha Perry-Fagant

PhD - Université de Montréal

Co-supervisor :

Salma Salhi

Master's Research - Université de Montréal

Sammy Nasser Sharief

Master's Research - Université de Montréal

Nolan Smyth

Postdoctorate - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Yitian Sun

Postdoctorate - McGill University

Co-supervisor :

Publications

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (published)

openreview.net

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

Alexandre Adam

Max Welling

Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass densit… (see more)y in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the recurrent inference machine to reconstruct simultaneously an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation.

2023-06-27

The Astrophysical Journal (published)

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

Ronan Legin

Alexandre Adam

2023-06-06

The Astrophysical Journal Letters (published)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

2023-04-24

ICML.cc/2023/Conference (poster)

openreview.net

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

Ronan Legin

Benjamin Wandelt

We report the application of implicit likelihood inference to the prediction of the macroparameters of strong lensing systems with neural ne… (see more)tworks. This allows us to perform deep-learning analysis of lensing systems within a well-defined Bayesian statistical framework to explicitly impose desired priors on lensing variables, obtain accurate posteriors, and guarantee convergence to the optimal posterior in the limit of perfect performance. We train neural networks to perform a regression task to produce point estimates of lensing parameters. We then interpret these estimates as compressed statistics in our inference setup and model their likelihood function using mixture density networks. We compare our results with those of approximate Bayesian neural networks, discuss their significance, and point to future directions. Based on a test set of 100,000 strong lensing simulations, our amortized model produces accurate posteriors for any arbitrary confidence interval, with a maximum percentage deviation of 1.4% at the 21.8% confidence level, without the need for any added calibration procedure. In total, inferring 100,000 different posteriors takes a day on a single GPU, showing that the method scales well to the thousands of lenses expected to be discovered by upcoming sky surveys.

2023-01-19

The Astrophysical Journal (published)

GaMPEN: A Machine-learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters

Aritra Ghosh

C. Urry

Amrit Rau

M. Cranmer

Kevin Schawinski

Dominic Stark

Chuan Tian

Ryan Ofman

T. Ananna

Connor Auge

Nico Cappelluti

D. Sanders

Ezequiel Treister

We introduce a novel machine-learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large num… (see more)bers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy’s bulge-to-total-light ratio (L B /L T ), effective radius (R e ), and flux (F). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a spatial transformer network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match z 0.25 galaxies in Hyper Suprime-Cam Wide g-band images, we demonstrate that GaMPEN achieves typical errors of 0.1 in L B /L T , 0.″17 (∼7%) in R e , and 6.3 × 104 nJy (∼1%) in F. GaMPEN's predicted uncertainties are well calibrated and accurate (5% deviation)—for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We a

2022-08-23

The Astrophysical Journal (published)