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Laurence Perreault-Levasseur

Membre académique associé

Professeure adjointe, Université de Montréal, Département de physique

Sujets de recherche

Apprentissage profond

Modèles génératifs

Modèles probabilistes

Réseaux de neurones en graphes

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

Tara Akhound-Sadegh

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siamak Ravanbakhsh

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Michael Matesic

Doctorat - UdeM

Guillaume Payeur

Doctorat - UdeM

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Sammy Nasser Sharief

Maîtrise recherche - UdeM

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - McGill

Co-superviseur⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Publications

The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies

Eirini Angeloudi

Marc Huertas-Company

Jesús Falcón-Barroso

Laurence Perreault-Levasseur

Alina Boecker

2025-10-21

Astronomy & Astrophysics (publié)

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images

Tri Nguyen

Laurence Perreault-Levasseur

J. Rose

Chris Lovell

Francisco Villaescusa-navarro

Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. … (voir plus)However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differences in the properties and environment of individual galaxies. We present a machine learning framework to infer the galaxy-specific predicted SHMF from galaxy images, conditioned on the assumed inverse warm DM particle mass

2025-10-16

ArXiv (prépublication)

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images

Tri Nguyen

Laurence Perreault-Levasseur

J. Rose

Chris Lovell

Francisco Villaescusa-navarro

2025-10-16

ArXiv (prépublication)

The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies

E. Angeloudi

Marc Huertas-Company

Jes'us Falc'on-Barroso

Laurence Perreault-Levasseur

A. Boecker

Understanding the origin of stars within a galaxy - whether formed in-situ or accreted from other galaxies (ex-situ) - is key to constrainin… (voir plus)g its evolution. Spatially resolving these components provides crucial insights into a galaxy's mass assembly history. We aim to predict the spatial distribution of ex-situ stellar mass fraction in MaNGA galaxies, and to identify distinct assembly histories based on the radial gradients of these predictions in the central regions. We employ a diffusion model trained on mock MaNGA analogs (MaNGIA), derived from the TNG50 cosmological simulation. The model learns to predict the posterior distribution of resolved ex-situ stellar mass fraction maps, conditioned on stellar mass density, velocity, and velocity dispersion gradient maps. After validating the model on an unseen test set from MaNGIA, we apply it to MaNGA galaxies to infer the spatially-resolved distribution of their ex-situ stellar mass fractions - i.e. the fraction of stellar mass in each spaxel originating from mergers. We identify four broad categories of ex-situ mass distributions: flat gradient, in-situ dominated; flat gradient, ex-situ dominated; positive gradient; and negative gradient. The vast majority of MaNGA galaxies fall in the first category - flat gradients with low ex-situ fractions - confirming that in-situ star formation is the main assembly driver for low- to intermediate-mass galaxies. At high stellar masses, the ex-situ maps are more diverse, highlighting the key role of mergers in building the most massive systems. Ex-situ mass distributions correlate with morphology, star-formation activity, stellar kinematics, and environment, indicating that accretion history is a primary factor shaping massive galaxies. Finally, by tracing their assembly histories in TNG50, we link each class to distinct merger scenarios, ranging from secular evolution to merger-dominated growth.

2025-09-29

ArXiv (prépublication)

caskade: building Pythonic scientific simulators

Laurence Perreault-Levasseur

2025-09-15

Journal of Open Source Software (publié)

Massive Extremely High-Velocity Outflow in the Quasar J164653.72+243942.2

Paola Rodríguez Hidalgo

Hyunseop 현섭 Choi 최

Patrick B. Hall

Karen M. Leighly

Liliana Flores

Mikel M. Charles

Cora DeFrancesco

J. Hlavacek-Larrondo

Laurence Perreault-Levasseur

2025-09-04

The Astrophysical Journal (publié)

Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing

Laurence Perreault-Levasseur

2025-08-20

The Astrophysical Journal (publié)

Field-level Comparison and Robustness Analysis of Cosmological <i>N</i>-body Simulations

Adrian E. Bayer

Francisco Villaescusa-navarro

Sammy Sharief

Romain Teyssier

Lehman H. Garrison

Laurence Perreault-Levasseur

Greg L. Bryan

Marco Gatti

Eli Visbal

2025-08-18

The Astrophysical Journal (publié)

Galaxy cluster characterization with machine learning techniques

M. Sadikov

J. Hlavacek-Larrondo

Laurence Perreault-Levasseur

C. L. Rhea

M. McDonald

M. Ntampaka

J. ZuHone

We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.

2025-06-03

The Astrophysical Journal (publié)

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization

Maximiliano Isi

Kaze W. K. Wong

Laurence Perreault-Levasseur

2025-05-29

The Astrophysical Journal Letters (publié)

Field-Level Comparison and Robustness Analysis of Cosmological N-Body Simulations

Adrian E. Bayer

Francisco Villaescusa-navarro

Sammy Nasser Sharief

Romain Teyssier

Lehman H. Garrison

Laurence Perreault-Levasseur

Greg L. Bryan

Marco Gatti

E. Visbal

2025-05-19

ArXiv (prépublication)

Field-Level Comparison and Robustness Analysis of Cosmological N-Body Simulations

Adrian E. Bayer

Francisco Villaescusa-navarro

Sammy Nasser Sharief

Romain Teyssier

Lehman H. Garrison

Laurence Perreault-Levasseur

Greg L. Bryan

Marco Gatti

E. Visbal

2025-05-19

ArXiv (prépublication)