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
PhD - McGill University
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Master's Research - Université de Montréal
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PhD - Université de Montréal
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Postdoctorate - Université de Montréal
PhD - Université de Montréal
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Master's Research - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Collaborating researcher - Université de Montréal
Master's Research - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Co-supervisor :
Master's Research - Université de Montréal
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

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
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.
Posterior samples of source galaxies in strong gravitational lenses with score-based priors
Alexandre Adam
Adam Coogan
Nikolay Malkin
Ronan Legin
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