Yashar Hezaveh

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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 :

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Misha Barth

PhD - Université de Montréal

Co-supervisor :

Noé Dia

Research Intern - Université de Montréal

Principal supervisor :

Dhvani Doshi

Master's Research - McGill University

Andreas Filipp

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

Postdoctorate - Université de Montréal

Principal supervisor :

Master's Research - Université de Montréal

Co-supervisor :

Pierre-Luc Bacon

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 :

Master's Research - McGill University

Yitian Sun

Postdoctorate - McGill University

Principal supervisor :

Wassim Tenachi

Postdoctorate - Université de Montréal

Co-supervisor :

Postdoctorate - Université de Montréal

Principal supervisor :

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Publications

TEMPLATES: Characterization of a Merger in the Dusty Lensing SPT0418-47 System

Jared Cathey

Anthony H. Gonzalez

Sidney Lower

Kedar A. Phadke

Justin Spilker

Manuel Aravena

Matthew Bayliss

Jack E. Birkin

Simon Birrer

Scott Chapman

Håkon Dahle

Christopher C. Hayward

Ryley Hill

Taylor A. Hutchison

Keunho J. Kim

Guillaume Mahler

Daniel P. Marrone

Desika Narayanan

Alexander Navarre … (see 7 more)

Cassie Reuter

Jane R Rigby

Keren Sharon

Manuel Solimano

Nikolaus Sulzenauer

Joaquin Vieira

David Vizgan

2024-05-12

The Astrophysical Journal (published)

Interpretable machine learning for finding intermediate-mass black holes

Mario Pasquato

PIERO TREVISAN

ABBAS ASKAR

GAIA CARENINI

MICHELA MAPELLI

Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained… (see more) on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to realistically simulate GCs, the simulation physics or initial conditions may fail to fully reflect reality. Therefore our training data may be biased, leading to a failure in generalization on observational data. Both the first issue -- explainability/interpretability -- and the second -- out of distribution generalization and fairness -- are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an XGBoost classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost and a global rule from CORELS.

2024-04-08

The Astrophysical Journal (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)

On Diffusion Modeling for Anomaly Detection

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (see more)n. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.

2024-01-15

ICLR.cc/2024/Conference (spotlight)

openreview.net

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)

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

Yordanka Apostolovski

Manuel Aravena

Timo Anguita

Matthieu Béthermin

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

2023-12-19

Astronomy & Astrophysics (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)

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)

openreview.net

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)