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

Missael Barco

Doctorat - UdeM

Co-superviseur⋅e :

Misha Barth

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Noé Dia

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Andreas Filipp

Doctorat - UdeM

Co-superviseur⋅e :

Ronan Legin

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Pablo Lemos

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Doctorat - UdeM

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

Salma Salhi

Doctorat - UdeM

Sammy Nasser Sharief

Maîtrise recherche - UdeM

Nolan Smyth

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Yitian Sun

Postdoctorat - McGill

Co-superviseur⋅e :

Justine Zeghal

Postdoctorat - UdeM

Co-superviseur⋅e :

Publications

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

Paola Rodríguez Hidalgo

Hyunseop Choi (최현섭)

Patrick B. Hall

Karen M. Leighly

Liliana Flores

Mikel M. Charles

Cora DeFrancesco

Julie Hlavacek-Larrondo

We present the analysis of one of the most extreme quasar outflows found to date in our survey of extremely high velocity outflows (EHVO). J… (voir plus)164653.72+243942.2 (z ~ 3.04) shows variable CIV1548,1551 absorption at speeds larger than 0.1c, accompanied by SiIV, NV and Lya, and disappearing absorption at lower speeds. We perform absorption measurements using the Apparent Optical Depth method and SimBAL. We find the absorption to be very broad (Δv ~35,100 km/s in the first epoch and ~13,000 km/s in the second one) and fast (vmax ~ -50,200 km/s and -49,000 km/s, respectively). We measure large column densities (

2025-09-03

The Astrophysical Journal (publié)

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

Andreas Filipp

We investigate the robustness of Neural Ratio Estimators (NREs) and Neural Posterior Estimators (NPEs) to distributional shifts in the conte… (voir plus)xt of measuring the abundance of dark matter subhalos using strong gravitational lensing data. While these data-driven inference frameworks can be accurate on test data from the same distribution as the training sets, in real applications, it is expected that simulated training data and true observational data will differ in their distributions. We explore the behavior of a trained NRE and trained sequential NPEs to estimate the population-level parameters of dark matter subhalos from a large sample of images of strongly lensed galaxies with test data presenting distributional shifts within and beyond the bounds of the training distribution in the nuisance parameters (e.g., the background source morphology). While our results show that NREs and NPEs perform well when tested perfectly in distribution, they exhibit significant biases when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs and NPEs to real astrophysical data, where high-dimensional underlying distributions are not perfectly known.

2025-08-19

The Astrophysical Journal (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

Greg L. Bryan

Marco Gatti

E. Visbal

2025-08-17

The Astrophysical Journal (publié)

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

Ronan Legin

Maximiliano Isi

Kaze W. K. Wong

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from t… (voir plus)his idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to ``clean'' non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations; alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade.

2025-05-28

The Astrophysical Journal Letters (publié)

The CASTOR mission

Patrick Côté

T. Woods

John Hutchings

J. Rhodes

R. Sánchez-Janssen

Alan D. Scott

J. Pazder

Melissa Amenouche

Michael Balogh

Simon Blouin

Alain Cournoyer

M. Drout

Nick Kuzmin

Katherine J. Mack

Laura Ferrarese

Wesley C. Fraser

S. Gallagher

Frederic J. Grandmont

Daryl Haggard

P. Harrison … (voir 160 de plus)

V. Hénault-Brunet

J. Kavelaars

V. Khatu

J. Roediger

J. Rowe

Marcin Sawicki

Jesper Skottfelt

Matt Taylor

L. van Waerbeke

Laurie Amen

Dhananjhay Bansal

Martin Bergeron

Toby Brown

Greg Burley

Hum Chand

Isaac Cheng

Ryan Cloutier

N. Dickson

Oleg Djazovski

Ivana Damjanov

James Doherty

K. Finner

Macarena García Del Valle Espinosa

Jennifer Glover

A. I. Gómez de Castro

Or Graur

Tim Hardy

Michelle Kao

D A Leahy

Deborah Lokhorst

A. I. Malz

Allison Man

Madeline A. Marshall

Sean McGee

Ryan McKenzie

Kai Michaud

Surhud S. More

David Morris

Patrick W. Morris

T. Moutard

Wasi Naqvi

Matthew Nicholl

G. Noirot

M. S. Oey

C. Opitom

Samir Salim

Bryan R. Scott

Charles Shapiro

Daniel Stern

Ashwin Subramaniam

David Thilke

I. Wevers

Dmitri Vorobiev

L. Y. Aaron Yung

Frédéric Zamkotsian

S. Aigrain

A. Alavi

Martin Barstow

Peter Bartosik

H. Bluhm

J. Bovy

Peter Cameron

R. Carlberg

J. Christiansen

Yuyang Chen

P. Crowther

Kristen Dage

Aaron Dotter

Patrick Dufour

Jean Dupuis

B. Dryer

A. Duara

Gwendolyn M. Eadie

Marielle R. Eduardo

V. Estrada-Carpenter

Sébastien Fabbro

A. Faisst

N. M. Ford

M. Fraser

Boris T. Gaensicke

Shashkiran Ganesh

Poshak Gandhi

Melissa L. Graham

R. Hamel

Martin Hellmich

John J. Hennessy

Kaitlyn Hessel

J. Heyl

Catherine Heymans

Renée Hložek

Michael Hoenk

Andrew Holland

Eric Huff

Ian Hutchinson

I. Iwata

April D. Jewell

Doug Johnstone

Maia Jones

Todd J. Jones

D. Lang

J. Lapington

Justin Larivière

C. Lawlor-Forsyth

Denis Laurin

Charles Lee

Ronan Legin

Ting S. Li

S. Lim

B. Ludwig

Matt Kozun

V. M

Robert Mann

Alan McConnachie

Evan McDonough

S. Metchev

David R. Miller

Takashi Moriya

Cameron Morgan

Julio F. Navarro

Y. Nazé

Shouleh Nikzad

Vivek Oad

N. N.-Q. Ouellette

E. Pass

Will J. Percival

Joe Postma

Nayyer Raza

G. T. Richards

Harvey Richer

Carmelle Robert

Erik Rosolowsky

J. Ruan

Sarah Rugheimer

S. Safi-Harb

Kanak Saha

Vicky Scowcroft

F. Sestito

Himanshu Sharma

James Sikora

G. Sivakoff

T. S. Sivarani

Patrick Smith

Warren Soh

R. Sorba

S. Subramanian

Hossen Teimoorinia

H. Teplitz

Shaylin Thadani

Shavon Thadani

Aaron Tohuvavohu

K. Venn

Nicholas Vieira

Jeremy J. Webb

P. Wiegert

Ryan Wierckx

Yanqin Wu

J. Yeung

S. K. Yi

2025-05-13

Journal of Astronomical Telescopes Instruments and Systems (publié)

Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL

Yoshua Bengio

Glen Berseth

Nikolay Malkin

This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (voir plus)L) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.

2025-03-05

ICLR.cc/2025/Workshop/DeLTa (poster)

openreview.net

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems

Gabriel Missael Barco

Alexandre Adam

Connor Stone

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-… (voir plus)level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations and showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates.

2025-02-05

The Astrophysical Journal (publié)

Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Tristan Deleu

Yu Luo

Changhyun Cho

Pablo Lemos

Yoshua Bengio

Xi Kang

Andrea Valerio Maccio

Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure… (voir plus) (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)--galaxy interaction has long been constrained by observed scaling relations, that is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date dataset, reveals a causal link between galaxy properties and dynamically-measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.

2025-01-27

The Astrophysical Journal (publié)

PQMass: Probabilistic Assessment of the Quality of Generative Models Using Probability Mass Estimation

Pablo Lemos

Sammy Sharief

Nikolay Malkin

Salma Salhi

Connor Stone

We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of gener… (voir plus)ative models. The proposed approach, PQMass, provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models. PQMass divides the sample space into non-overlapping regions and applies chi-squared tests to the number of data samples that fall within each region, giving a

2025-01-21

International Conference on Learning Representations (poster)

openreview.net

IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Anna M. M. Scaife

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.

2025-01-04

ArXiv (prépublication)

Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine

C. L. Rhea

J. Hlavacek-Larrondo

Alexandre Adam

Ralph P. Kraft

Ákos Bogdán

Marine Prunier

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new fr… (voir plus)ontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-

2024-09-15

ArXiv (prépublication)

Improving Gradient-Guided Nested Sampling for Posterior Inference

Will Handley