Laurence Perreault-Levasseur

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Biographie

Laurence Perreault-Levasseur est titulaire de la Chaire de recherche du Canada en cosmologie computationnelle et en intelligence artificielle. Elle est professeure adjointe à l'Université de Montréal et membre associée de Mila – Institut québécois d’intelligence artificielle, où elle mène des recherches sur le développement et l'application de méthodes d'apprentissage automatique à la cosmologie. Elle est également chercheuse invitée au Flatiron Institute, à New York. Auparavant, elle a été chargée de recherche au Center for Computational Astrophysics du Flatiron Institute et boursière postdoctorale du KIPAC à l'Université de Stanford. Laurence Perreault-Levasseur a obtenu un doctorat de l'Université de Cambridge, où elle a travaillé sur les applications des méthodes de la théorie des champs effectifs ouverts au formalisme de l'inflation. Elle est titulaire d'une licence et d'une maîtrise en sciences de l'Université McGill.

Étudiants actuels

Doctorat - UdeM

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siamak Ravanbakhsh

eirini angeloudi

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Missael Barco

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Misha Barth

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Antoine Bourdin

Stagiaire de recherche - McGill

Co-superviseur⋅e :

Joseph Choi

Postdoctorat - UdeM

Etienne Collin

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

contact@etiennecollin.com

Noé Dia

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Andreas Filipp

Doctorat - UdeM

Co-superviseur⋅e :

andreas.filipp@umontreal.ca

Patrick Janulewicz

Maîtrise recherche - McGill

Rawan Karam

Maîtrise recherche - UdeM

Jenna Karcheski

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Stephanie Lee

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Ronan Legin

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Pablo Lemos

Postdoctorat - UdeM

Co-superviseur⋅e :

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Parker Levesque

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Visiteur de recherche indépendant - University of Padua

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

Salma Salhi

Maîtrise recherche - UdeM

Sammy Nasser Sharief

Maîtrise recherche - UdeM

Nolan Smyth

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Yitian Sun

Postdoctorat - McGill

Co-superviseur⋅e :

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 (publié)

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… (voir plus)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 (publié)

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

Ronan Legin

Alexandre Adam

2023-06-06

The Astrophysical Journal Letters (publié)

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… (voir plus)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 (publié)

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… (voir plus)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 (publié)