Yashar Hezaveh

Google Scholar

Biography

Yashar Hezaveh is an associate academic member of Mila – Quebec Artificial Intelligence Institute and director of the Montréal Institute for Astrophysical Data Analysis and Machine Learning (Ciela). He is an assistant professor in the Department of Physics at Université de Montréal and the Canada Research Chair in Astrophysical Data Analysis and Machine Learning. In addition, Hezaveh is an associate member of McGill University’s Trottier Space Institute, and a visiting fellow at the Center for Computational Astrophysics at Flatiron Institute in New York and at the Perimeter Institute for Theoretical Physics in Waterloo, Ontario. He was previously a research fellow at the Flatiron Institute (2018–2019) and a NASA Hubble Fellow at Stanford University (2013–2018).

Hezaveh is a world leader in the analysis of astrophysical data using deep learning. His current research focuses primarily on Bayesian inference in AI, the goal being to learn about the distribution of dark matter in strongly lensed galaxies using data from large cosmological surveys. His research is supported by the Schmidt Futures Foundation and the Simons Foundation.

Current Students

eirini angeloudi

Research Intern - Université de Montréal

Principal supervisor :

Missael Barco

Master's Research - Université de Montréal

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Google Scholar

Misha Barth

PhD - Université de Montréal

Co-supervisor :

Antoine Bourdin

Research Intern - McGill University

Principal supervisor :

Etienne Collin

Research Intern - Université de Montréal

Co-supervisor :

contact@etiennecollin.com

Franco Del Balso

Research Intern - Université de Montréal

Noé Dia

Research Intern - Université de Montréal

Principal supervisor :

Dhvani Doshi

Master's Research - McGill University

Andreas Filipp

PhD - Université de Montréal

Principal supervisor :

andreas.filipp@umontreal.ca

Jenna Karcheski

Research Intern - Université de Montréal

Co-supervisor :

Stephanie Lee

Research Intern - Université de Montréal

Co-supervisor :

Ronan Legin

PhD - Université de Montréal

Co-supervisor :

Pablo Lemos

Postdoctorate - Université de Montréal

Principal supervisor :

Research Intern - Université de Montréal

Principal supervisor :

Parker Levesque

Master's Research - Université de Montréal

Principal supervisor :

Hasti Nafisi

Master's Research - Université de Montréal

Principal supervisor :

Guy Wolf

Nicolas Payot

Master's Research - Université de Montréal

Co-supervisor :

Sacha Perry-Fagant

PhD - Université de Montréal

Principal supervisor :

Nolan Smyth

Postdoctorate - Université de Montréal

Co-supervisor :

Postdoctorate - Université de Montréal

Principal supervisor :

Yitian Sun

Postdoctorate - McGill University

Principal supervisor :

Justine Zeghal

Postdoctorate - Université de Montréal

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Publications

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… (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.

2023-11-03

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

openreview.net

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

2023-10-13

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

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 (…

2023-09-27

ArXiv (preprint)

AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images

Connor J Stone

Stéphane Courteau

Jean-Charles Cuillandre

Nikhil Arora

2023-08-16

Monthly Notices of the Royal Astronomical Society (published)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

2023-07-03

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

openreview.net

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

Alexandre Adam

Max Welling

Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass densit… (see more)y in 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 to reconstruct simultaneously 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.

2023-06-27

The Astrophysical Journal (published)

Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise

Ronan Legin

Alexandre Adam

2023-06-06

The Astrophysical Journal Letters (published)

Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy

Justin Spilker

Kedar A. Phadke

Manuel Aravena

Melanie Archipley

Matthew Bayliss

Jack E. Birkin

Matthieu Béthermin

James R. Burgoyne

Jared Cathey

Scott Chapman

Håkon Dahle

Anthony H. Gonzalez

Gayathri Gururajan

Christopher C Hayward

Ryley Hill

Taylor A. Hutchison

Keunho J. Kim

Seonwoo Kim

D. Law … (see 19 more)

Ronan Legin

M. Malkan

Daniel P. Marrone

E. Murphy

Desika Narayanan

Alexander Navarre

Grace M. Olivier

J. Rich

Jane R Rigby

Cassie Reuter

J. Rhoads

Keren Sharon

J. Smith

Manuel Solimano

Nikolaus Sulzenauer

Joaquin Vieira

David Vizgan

Axel Weiß

K. Whitaker

2023-06-05

Nature (published)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

2023-04-24

ICML.cc/2023/Conference (poster)

openreview.net

Spectroscopy of CASSOWARY gravitationally-lensed galaxies in SDSS: characterisation of an extremely bright reionization-era analog at z = 1.42

Ramesh Mainali

Daniel P Stark

Tucker Jones

Richard S Ellis

Jane R Rigby

We present new observations of sixteen bright (r = 19 − 21) gravitationally lensed galaxies at z ≃ 1 − 3 selected from the CASSOWARY s… (see more)urvey. Included in our sample is the z = 1.42 galaxy CSWA-141, one of the brightest known reionization-era analogs at high redshift (g=20.5), with a large sSFR (31.2 Gyr−1) and an [OIII]+Hβ equivalent width (EW[OIII] + Hβ=730 Å) that is nearly identical to the average value expected at z ≃ 7 − 8. In this paper, we investigate the rest-frame UV nebular line emission in our sample with the goal of understanding the factors that regulate strong CIII] emission. Whereas most of the sources in our sample show weak UV line emission, we find elevated CIII] in the spectrum of CSWA-141 (EWCIII]=4.6±1.9 Å) together with detections of other prominent emission lines (OIII], Si III], Fe II⋆, Mg II). We compare the rest-optical line properties of high redshift galaxies with strong and weak CIII] emission, and find that systems with the strongest UV line emission tend to have young stellar populations and nebular gas that is moderately metal-poor and highly ionized, consistent with trends seen at low and high redshift. The brightness of CSWA-141 enables detailed investigation of the extreme emission line galaxies which become common at z > 6. We find that gas traced by the CIII] doublet likely probes higher densities than that traced by [OII] and [SII]. Characterisation of the spectrally resolved Mg II emission line and several low ionization absorption lines suggests neutral gas around the young stars is likely optically thin, potentially facilitating the escape of ionizing radiation.

2023-02-08

Monthly Notices of the Royal Astronomical Society (published)

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

Ronan Legin

Benjamin Wandelt

We report the application of implicit likelihood inference to the prediction of the macroparameters of strong lensing systems with neural ne… (see more)tworks. 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, obtain accurate posteriors, and 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 1.4% at the 21.8% confidence level, without the need for any added calibration procedure. In total, inferring 100,000 different posteriors takes a day on a single GPU, showing that the method scales well to the thousands of lenses expected to be discovered by upcoming sky surveys.

2023-01-19

The Astrophysical Journal (published)