Portrait de Laurence Perreault-Levasseur

Laurence Perreault-Levasseur

Membre académique principal

levassel@mila.quebec

Professeure adjointe, Université de Montréal, Département de physique

Sujets de recherche

Apprentissage profond

Inférence bayésienne

Modèles génératifs

Modèles probabilistes

Systèmes dynamiques

Vision par ordinateur

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

alexandre.adam@mila.quebec

Tara Akhound-Sadegh

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siamak Ravanbakhsh

tara.akhoundsadegh@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

missael-gabriel.barco@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

michael.barth@mila.quebec

Mohammed Chamma

Postdoctorat - McGill

mohammed.chamma@mila.quebec

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

noe.dia@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

andreas.filipp@umontreal.ca

Doctorat - UdeM

Superviseur⋅e principal⋅e :

ronan.legin@mila.quebec

Christian Lemay

Stagiaire de recherche - UdeM

christian.lemay1@sympatico.ca

Postdoctorat - UdeM

Co-superviseur⋅e :

pablo.lemos@mila.quebec

Michael Matesic

Doctorat - UdeM

michael.matesic@mila.quebec

Guillaume Payeur

Doctorat - UdeM

guillaume.payeur@mail.mcgill.ca

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

nicolas.payot@mila.quebec

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

sacha.perry-fagant@mila.quebec

Doctorat - UdeM

salhi.salma@mila.quebec

Postdoctorat - McGill

ketan.sand@mila.quebec

Sammy Nasser Sharief

Maîtrise recherche - UdeM

sammy.sharief@mila.quebec

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

nolan.smyth@mila.quebec

Postdoctorat - McGill

Co-superviseur⋅e :

yitian.sun@mila.quebec

Postdoctorat - UdeM

Co-superviseur⋅e :

justine.zeghal@mila.quebec

Publications

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

Laurence Perreault-Levasseur

Carolina Cuesta-Lazaro

Chirag Modi

2023-11-28

arXiv (prépublication)

Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra

C. L. Rhea

J. Hlavacek-Larrondo

Ralph P. Kraft

Ákos Bogdán

Laurence Perreault-Levasseur

2023-11-28

ArXiv (prépublication)

The search for the lost attractor

Mario Pasquato

Syphax Haddad

Pierfrancesco Di Cintio

Mircea Petrache

Ugo Niccolò Di Carlo

Alessandro Alberto Trani

Laurence Perreault-Levasseur

2023-11-26

arXiv (prépublication)

Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise

Laurence Perreault-Levasseur

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (voir plus) of complex, high-dimensional, non-Gaussian, and anisotropic noise. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.

2023-11-02

NeurIPS.cc/2023/Workshop/Deep_Inverse (poster)

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Matthew Ho

Laurence Perreault-Levasseur

Shirley Ho

Benjamin Wandelt

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of th… (voir plus)e universe have provided a way to infer initial conditions consistent with present-day observations. However, due to the high complexity of the inference problem, these methods either fail to sample a distribution of possible initial density fields or require significant approximations in the simulation model to be tractable, potentially leading to biased results. In this work, we propose the use of score-based generative models to sample realizations of the early universe given present-day observations. We infer the initial density field of full high-resolution dark matter N-body simulations from the present-day density field and verify the quality of produced samples compared to the ground truth based on summary statistics. The proposed method is capable of providing plausible realizations of the early universe density field from the initial conditions posterior distribution marginalized over cosmological parameters and can sample orders of magnitude faster than current state-of-the-art methods.

2023-10-03

Monthly Notices of the Royal Astronomical Society: Letters (publié)

Time Delay Cosmography with a Neural Ratio Estimator

Ève Campeau-Poirier

Laurence Perreault-Levasseur

We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…

2023-09-26

ArXiv (prépublication)

ASTROPHOT: fitting everything everywhere all at once in astronomical images

Connor J. Stone

Stéphane Courteau

Jean-Charles Cuillandre

Laurence Perreault-Levasseur

Nikhil Arora

We present AstroPhot, a fast, powerful, and user-friendly Python based astronomical image photometry solver. AstroPhot incorporates automati… (voir plus)c differentiation and GPU (or parallel CPU) acceleration, powered by the machine learning library PyTorch. Everything: AstroPhot can fit models for sky, stars, galaxies, PSFs, and more in a principled Chi^2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: AstroPhot can optimize forward models on CPU or GPU; across images that are large, multi-band, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg-Marquardt, Gradient descent, and No-U-Turn MCMC sampling. With an object-oriented user interface, AstroPhot makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the AstroPhot code and compares it to other popular astronomical image modeling software. AstroPhot is open-source, fully Python based, and freely accessible here: https://github.com/Autostronomy/AstroPhot

2023-08-15

Monthly Notices of the Royal Astronomical Society (inconnu)

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

Aritra Ghosh

C. Megan Urry

Aayush Mishra

Laurence Perreault-Levasseur

Priyamvada Natarajan

David B. Sanders

Daisuke Nagai

Chuan Tian

Nico Cappelluti

Jeyhan S. Kartaltepe

Meredith C. Powell

Amrit Rau

Ezequiel Treister

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for …

2023-08-13

The Astrophysical Journal (publié)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Laurence Perreault-Levasseur

Parameter inference, i.e. inferring the posterior distribution of the parameters of a statistical model given some data, is a central proble… (voir plus)m to many scientific disciplines. Generative models can be used as an alternative to Markov Chain Monte Carlo methods for conducting posterior inference, both in likelihood-based and simulation-based problems. However, assessing the accuracy of posteriors encoded in generative models is not straightforward. In this paper, we introduce `Tests of Accuracy with Random Points' (TARP) coverage testing as a method to estimate coverage probabilities of generative posterior estimators. Our method differs from previously-existing coverage-based methods, which require posterior evaluations. We prove that our approach is necessary and sufficient to show that a posterior estimator is accurate. We demonstrate the method on a variety of synthetic examples, and show that TARP can be used to test the results of posterior inference analyses in high-dimensional spaces. We also show that our method can detect inaccurate inferences in cases where existing methods fail.

2023-07-02

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

proceedings.mlr.press

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

Laurence Perreault-Levasseur

MAX WELLING

Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass de… (voir plus)nsity in the 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 (RIM) to simultaneously reconstruct 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.

2023-06-26

The Astrophysical Journal (publié)

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

Laurence Perreault-Levasseur

Benjamin Wandelt

We report the application of implicit likelihood inference to the prediction of the macro-parameters of strong lensing systems with neural n… (voir plus)etworks. 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, to obtain accurate posteriors, and to 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

2023-01-18

The Astrophysical Journal (publié)

Posterior samples of source galaxies in strong gravitational lenses with score-based priors

Nikolay Malkin

Laurence Perreault-Levasseur

Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge,… (voir plus) in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.

2022-11-06

ArXiv (prépublication)