Laurence Perreault-Levasseur

Google Scholar

Andreas Filipp

PhD - Université de Montréal

Co-supervisor :

andreas.filipp@mila.quebec

Antoine Bourdin

Research Intern - McGill University

Co-supervisor :

antoine.bourdin@mila.quebec

Connor Stone

Postdoctorate - Université de Montréal

Co-supervisor :

connor.stone@mila.quebec

Etienne Collin

Research Intern - Université de Montréal

Principal supervisor :

etienne.collin@mila.quebec

Joseph Choi

Postdoctorate - Université de Montréal

joseph.choi@mila.quebec

Jenna Karcheski

Research Intern - Université de Montréal

Principal supervisor :

jenna.karcheski@mila.quebec

Laura Leuzzi

Research Intern - Université de Montréal

Co-supervisor :

mario.pasquato@mila.quebec

laura.leuzzi@mila.quebec

Mario Pasquato

Independent visiting researcher - University of Padua

Google Scholar

Misha Barth

PhD - Université de Montréal

Principal supervisor :

michael.barth@mila.quebec

michael.matesic@mila.quebec

Michael Matesic

PhD - Université de Montréal

Missael Barco

Master's Research - Université de Montréal

Co-supervisor :

missael-gabriel.barco@mila.quebec

Master's Research - Université de Montréal

Principal supervisor :

nicolas.payot@mila.quebec

Noé Dia

Research Intern - Université de Montréal

noe.dia@mila.quebec

Pablo Lemos

Postdoctorate - Université de Montréal

Co-supervisor :

pablo.lemos@mila.quebec

Master's Research - Université de Montréal

Co-supervisor :

parker.levesque@mila.quebec

patrick.janulewicz@mila.quebec

Patrick Janulewicz

Master's Research - McGill University

Rawan Karam

Master's Research - Université de Montréal

rawan.karam@mila.quebec

Ronan Legin

PhD - Université de Montréal

Principal supervisor :

ronan.legin@mila.quebec

Sacha Perry-Fagant

PhD - Université de Montréal

Co-supervisor :

sacha.perry-fagant@mila.quebec

sammy.sharief@mila.quebec

Salma Salhi

Master's Research - Université de Montréal

salhi.salma@mila.quebec

Sammy Nasser Sharief

Master's Research - Université de Montréal

tara.akhoundsadegh@mila.quebec

Tara Akhound-Sadegh

PhD - McGill University

Principal supervisor :

Siamak Ravanbakhsh

Stephanie Lee

Research Intern - Université de Montréal

Principal supervisor :

yung-hsin.lee@mila.quebec

Publications

Lie Point Symmetry and Physics-Informed Networks

Tara Akhound-Sadegh

Johannes Brandstetter

Max Welling

Siamak Ravanbakhsh

Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equiv… (see more)ariant architectures. However, despite their potential, their integration into neural solvers for partial differential equations (PDEs) remains largely unexplored. We explore the integration of PDE symmetries, known as Lie point symmetries, in a major family of neural solvers known as physics-informed neural networks (PINNs). We propose a loss function that informs the network about Lie point symmetries in the same way that PINN models try to enforce the underlying PDE through a loss function. Intuitively, our symmetry loss ensures that the infinitesimal generators of the Lie group conserve the PDE solutions.. Effectively, this means that once the network learns a solution, it also learns the neighbouring solutions generated by Lie point symmetries. Empirical evaluations indicate that the inductive bias introduced by the Lie point symmetries of the PDEs greatly boosts the sample efficiency of PINNs.

openreview.net

AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images

Connor J Stone

Stéphane Courteau

Jean-Charles Cuillandre

Nikhil Arora

2023-08-16

Monthly Notices of the Royal Astronomical Society (published)

Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey

Aritra Ghosh

C. Urry

Aayush Mishra

P. Natarajan

D. Sanders

Daisuke Nagai

Chuan Tian

Nico Cappelluti

J. Kartaltepe

M. Powell

Amrit Rau

Ezequiel Treister

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for ∼… (see more)8 million galaxies in the Hyper Suprime-Cam Wide survey with z ≤ 0.75 and m ≤ 23. GaMPEN is a machine-learning framework that estimates Bayesian posteriors for a galaxy’s bulge-to-total light ratio (L B /L T ), effective radius (R e ), and flux (F). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with 1% of our data set. This two-step process will be critical for applying machine-learning algorithms to future large imaging surveys, such a

2023-08-14

The Astrophysical Journal (published)

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)

Strong gravitational lensing as a probe of dark matter

Simona Vegetti

Simon Birrer

Giulia Despali

C. Fassnacht

Daniel A. Gilman

J. McKean

D. Powell

Conor M. O'riordan

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.

2023-06-20

ArXiv (preprint)

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)