Laurence Perreault-Levasseur

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Biography

Laurence Perreault-Levasseur is the Canada Research Chair in Computational Cosmology and Artificial Intelligence. She is an assistant professor at Université de Montréal and an associate academic member of Mila – Quebec Artificial Intelligence Institute. Perreault-Levasseur’s research focuses on the development and application of machine learning methods to cosmology.

She is also a Visiting Scholar at the Flatiron Institute in New York City. Prior to that, she was a research fellow at their Center for Computational Astrophysics, and a KIPAC postdoctoral fellow at Stanford University.

For her PhD degree at the University of Cambridge, she worked on applications of open effective field theory methods to the formalism of inflation. She completed her BSc and MSc degrees at McGill University.

Current Students

PhD - Université de Montréal

Google Scholar

Tara Akhound-Sadegh

PhD - McGill University

Principal supervisor :

Siamak Ravanbakhsh

Missael Barco

PhD - Université de Montréal

Co-supervisor :

Misha Barth

PhD - Université de Montréal

Principal supervisor :

Noé Dia

Research Intern - Université de Montréal

Co-supervisor :

Andreas Filipp

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Guillaume Payeur

PhD - Université de Montréal

Master's Research - Université de Montréal

Principal supervisor :

Sacha Perry-Fagant

PhD - Université de Montréal

Co-supervisor :

Salma Salhi

PhD - Université de Montréal

Sammy Nasser Sharief

Master's Research - Université de Montréal

Nolan Smyth

Postdoctorate - Université de Montréal

Principal supervisor :

Yitian Sun

Postdoctorate - McGill University

Co-supervisor :

Justine Zeghal

Postdoctorate - Université de Montréal

Co-supervisor :

Publications

Assessing the Viability of Generative Modeling in Simulated Astronomical Observations

Patrick Janulewicz

Tracy Webb

In this paper, we use methods for assessing the quality of generative models and apply them to a problem from the physical sciences. We turn… (see more) our attention to astrophysics, where cosmological simulations are often used to create mock observations that mimic telescope images. These simulations and their mock observations are often slow and challenging to generate, inspiring some to use generative modeling to enhance the amount of data available to study. In this work, we add realism to simulated images of galaxy clusters and use probability mass estimation to assess their fidelity compared to reality. We find that the simulations are biased compared to real observations and suggest that researchers applying generative modeling to these systems should proceed with caution.

2024-06-16

ICML.cc/2024/Workshop/SPIGM (poster)

Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing

Andreas Filipp

In recent years, there has been increasing interest in the field of astrophysics in applying Neural Ratio Estimators (NREs) to large-scale i… (see more)nference problems where both amortization and marginalization over a large number of nuisance parameters are needed. Here, in order to assess the true potential of this method to produce unbiased inference on real data, we investigate the robustness of NREs to distribution shifts and model misspecification in the specific scientific application of the measurement of dark matter population-level parameters using strong gravitational lensing. We investigate the behaviour of a trained NRE for test data presenting distributional shifts inside the bounds of training, as well as out of distribution, both in the linear and non-linear parameters of this problem. While our results show that NREs perform when tested perfectly in distribution, we find that they exhibit significant biases and drawbacks when confronted with slight deviations from the examples seen in the training distribution. This indicates the necessity for caution when applying NREs to real astrophysical data, where underlying distributions are not perfectly known and models do not perfectly reconstruct the true underlying distributions.

2024-06-16

ICML.cc/2024/Workshop/SPIGM (poster)

Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics

Matthew Ho

2024-06-15

ICML.cc/2024/Workshop/AI4Science (poster)

Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at $z\sim6.6$

Manuela Bischetti

Hyunseop 현섭 Choi 최

Fabrizio Fiore

Chiara Feruglio

Stefano Carniani

Valentina D'Odorico

Eduardo Banados

Huanqing Chen

Roberto Decarli

Simona Gallerani

J. Hlavacek-Larrondo

Samuel Lai

Karen M. Leighly

Chiara Mazzucchelli

Roberta Tripodi

Fabian Walter

Feige Wang

Jinyi Yang

Maria Vittoria Zanchettin … (see 1 more)

Yongda Zhu

2024-04-17

ArXiv (preprint)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Charles Wilson

Gabriel Missael Barco

2023-12-31

Journal of Open Source Software (published)

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… (see more)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.

2023-11-28

ArXiv (preprint)

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Micah Bowles

A. Scaife

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-28

arXiv (preprint)

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

Nicolas Payot

Carolina Cuesta-lazaro

C. Modi

2023-11-28

ArXiv (preprint)

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

C. L. Rhea

J. Hlavacek-Larrondo

Ralph P. Kraft

Ákos Bogdán

2023-11-28

ArXiv (preprint)

The search for the lost attractor

Mario Pasquato

Syphax Haddad

Pierfrancesco Di Cintio

Noé Dia

Mircea Petrache

Ugo Niccolò Di Carlo

Alessandro Alberto Trani

2023-11-26

arXiv (preprint)

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

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (see more) 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

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… (see more)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 (published)