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Laurence Perreault-Levasseur

Membre académique associé
Professeure adjointe, Université de Montréal, Département de physique

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 - Université de Montréal
Doctorat - Université de Montréal
Co-superviseur⋅e :
Stagiaire de recherche - McGill University
Co-superviseur⋅e :
Postdoctorat - Université de Montréal
Co-superviseur⋅e :
Stagiaire de recherche - Université de Montréal
Superviseur⋅e principal⋅e :
Postdoctorat - Université de Montréal
Stagiaire de recherche - Université de Montréal
Co-superviseur⋅e :
Visiteur de recherche indépendant - University of Padua
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Doctorat - Université de Montréal
Maîtrise recherche - Université de Montréal
Co-superviseur⋅e :
Maîtrise recherche - Université de Montréal
Superviseur⋅e principal⋅e :
Stagiaire de recherche - Université de Montréal
Postdoctorat - Université de Montréal
Co-superviseur⋅e :
Maîtrise recherche - Université de Montréal
Co-superviseur⋅e :
Maîtrise recherche - McGill University
Maîtrise recherche - Université de Montréal
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Doctorat - Université de Montréal
Co-superviseur⋅e :
Maîtrise recherche - Université de Montréal
Maîtrise recherche - Université de Montréal
Doctorat - McGill University
Superviseur⋅e principal⋅e :
Stagiaire de recherche - University of Toronto
Superviseur⋅e principal⋅e :

Publications

Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at $z\sim6.6$
Manuela Bischetti
Hyunseop Choi
Fabrizio Fiore
Chiara Feruglio
Stefano Carniani
Valentina D'odorico
Eduardo Banados
Huanqing Chen
Roberto Decarli
Simona Gallerani
Julie Hlavacek-larrondo
Samuel Lai
K. Leighly
Chiara Mazzucchelli
Roberta Tripodi
Fabian Walter
Feige Wang
Jinyi Yang
Maria Vittoria Zanchettin … (voir 1 de plus)
Yongda Zhu
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Pablo Lemos
Sammy N. Sharief
Nikolay Malkin
Improving Gradient-guided Nested Sampling for Posterior Inference
Pablo Lemos
Will Handley
Nikolay Malkin
We present a performant, general-purpose gradient-guided nested sampling algorithm, …
Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem
Nicolas Payot
Mario Pasquato
Alessandro A. Trani
Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to pred… (voir plus)ict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active Learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem's decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology.
Bayesian Imaging for Radio Interferometry with Score-Based Priors
No'e Dia
M. J. Yantovski-Barth
Alexandre Adam
Micah Bowles
Pablo Lemos
A. Scaife
U. Montŕeal
Ciela Institute
Flatiron Institute
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Alexandre Adam
Connor Stone
Connor Bottrell
Ronan Legin
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (voir plus) barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.
Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot
Pablo Lemos
Carolina Cuesta-lazaro
C. Modi
Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra
C. Rhea
Julie Hlavacek-larrondo
Ralph P. Kraft
Ákos Bogdán
Alexandre Adam
The search for the lost attractor
Mario Pasquato
Syphax Haddad
Pierfrancesco Di Cintio
Alexandre Adam
Pablo Lemos
No'e Dia
Mircea Petrache
Ugo Niccolo Di Carlo
Alessandro A. Trani
Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise
Ronan Legin
Alexandre Adam
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.
Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models
Ronan Legin
Matthew Ho
Pablo Lemos
Shirley Ho
Benjamin Wandelt
Time Delay Cosmography with a Neural Ratio Estimator
Eve Campeau-Poirier
Adam Coogan
We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…