Yashar Hezaveh

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

Missael Barco

PhD - Université de Montréal

Principal supervisor :

missael-gabriel.barco@mila.quebec

Misha Barth

PhD - Université de Montréal

Co-supervisor :

michael.barth@mila.quebec

Dhvani Doshi

Master's Research - McGill University

dhvani.doshi@mila.quebec

Andreas Filipp

PhD - Université de Montréal

Principal supervisor :

andreas.filipp@umontreal.ca

Ronan Legin

PhD - Université de Montréal

Co-supervisor :

ronan.legin@mila.quebec

Nicolas Payot

Master's Research - Université de Montréal

Co-supervisor :

nicolas.payot@mila.quebec

Sacha Perry-Fagant

PhD - Université de Montréal

Principal supervisor :

sacha.perry-fagant@mila.quebec

Nolan Smyth

Postdoctorate - Université de Montréal

Co-supervisor :

nolan.smyth@mila.quebec

Yitian Sun

Postdoctorate - McGill University

Principal supervisor :

yitian.sun@mila.quebec

wassim.tenachi@mila.quebec

Wassim Tenachi

Postdoctorate - Université de Montréal

Co-supervisor :

Pierre-Luc Bacon

Justine Zeghal

Postdoctorate - Université de Montréal

Principal supervisor :

justine.zeghal@mila.quebec

Publications

Extended Lyman-alpha emission towards the SPT2349-56 protocluster at $z=4.3$

Yordanka Apostolovski

Manuel Aravena

Timo Anguita

Matthieu Bethermin

James R. Burgoyne

Scott Chapman

C. Breuck

Anthony R Gonzalez

Max Gronke

Lucia Guaita

Ryley Hill

Sreevani Jarugula

E. Johnston

M. Malkan

Desika Narayanan

Cassie Reuter

Manuel Solimano

Justin Spilker

Nikolaus Sulzenauer … (see 3 more)

Joaquin Vieira

David Vizgan

Axel Weiß

Deep spectroscopic surveys with the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed that some of the brightest infrared so… (see more)urces in the sky correspond to concentrations of submillimeter galaxies (SMGs) at high redshift. Among these, the SPT2349-56 protocluster system is amongst the most extreme examples given its high source density and integrated star formation rate. We conducted a deep Lyman-alpha line emission survey around SPT2349-56 using the Multi-Unit Spectroscopic Explorer (MUSE) at the Very Large Telescope (VLT) in order to characterize this uniquely dense environment. Taking advantage of the deep three-dimensional nature of this survey, we performed a sensitive search for Lyman-alpha emitters (LAEs) toward the core and northern extension of the protocluster, which correspond to the brightest infrared regions in this field. Using a smoothed narrowband image extracted from the MUSE datacube around the protocluster redshift, we searched for possible extended structures. We identify only three LAEs at

2024-03-05

Astronomy & Astrophysics (published)

Searching for Strong Gravitational Lenses

Cameron Lemon

Frederic Courbin

Anupreeta More

Paul Schechter

Raoul Cañameras

Ludovic Delchambre

Calvin Leung

Yiping Shu

Chiara Spiniello

Jonas Klüter

Richard G. McMahon

2024-02-20

Space Science Reviews (published)

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

Nicolas Payot

Mario Pasquato

Alessandro Alberto 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

Chirag Modi

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

Ronan Legin

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)

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

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)

Time Delay Cosmography with a Neural Ratio Estimator

Ève Campeau-Poirier

Adam Coogan

We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…

2023-09-26

ArXiv (preprint)

ASTROPHOT: fitting everything everywhere all at once in astronomical images

Connor J. Stone

Stéphane Courteau

Jean-Charles Cuillandre

Nikhil Arora

We present AstroPhot, a fast, powerful, and user-friendly Python based astronomical image photometry solver. AstroPhot incorporates automati… (see more)c differentiation and GPU (or parallel CPU) acceleration, powered by the machine learning library PyTorch. Everything: AstroPhot can fit models for sky, stars, galaxies, PSFs, and more in a principled Chi^2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: AstroPhot can optimize forward models on CPU or GPU; across images that are large, multi-band, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg-Marquardt, Gradient descent, and No-U-Turn MCMC sampling. With an object-oriented user interface, AstroPhot makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the AstroPhot code and compares it to other popular astronomical image modeling software. AstroPhot is open-source, fully Python based, and freely accessible here: https://github.com/Autostronomy/AstroPhot

2023-08-15

Monthly Notices of the Royal Astronomical Society (unknown)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Adam Coogan

Parameter inference, i.e. inferring the posterior distribution of the parameters of a statistical model given some data, is a central proble… (see more)m to many scientific disciplines. Generative models can be used as an alternative to Markov Chain Monte Carlo methods for conducting posterior inference, both in likelihood-based and simulation-based problems. However, assessing the accuracy of posteriors encoded in generative models is not straightforward. In this paper, we introduce `Tests of Accuracy with Random Points' (TARP) coverage testing as a method to estimate coverage probabilities of generative posterior estimators. Our method differs from previously-existing coverage-based methods, which require posterior evaluations. We prove that our approach is necessary and sufficient to show that a posterior estimator is accurate. We demonstrate the method on a variety of synthetic examples, and show that TARP can be used to test the results of posterior inference analyses in high-dimensional spaces. We also show that our method can detect inaccurate inferences in cases where existing methods fail.

2023-07-24

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

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

MAX WELLING

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

2023-06-26

The Astrophysical Journal (published)