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 :

Pablo Lemos

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

Transformer Embeddings for Fast Microlensing Inference

Nolan Smyth

2025-12-11

ArXiv (preprint)

Neural Deprojection of Galaxy Stellar Mass Profiles

M. J. Yantovski-Barth

Hengyue Zhang

Nolan Smyth

Martin Bureau

We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable… (see more) mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.

2025-11-24

ArXiv (preprint)

Mind the Information Gap: Unveiling Detailed Morphologies of z 0.5-1.0 Galaxies with SLACS Strong Lenses and Data-Driven Analysis

Alexandre Adam

Gabriel Missael Barco

Adam Coogan

Nikolay Malkin

2025-11-23

ArXiv (preprint)

Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images

Gabriel Missael Barco

We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF charact… (see more)erization is critical for precision measurements in many fields including: weak lensing, astrometry, and photometry. Our method defines the posterior distribution of the pixelized PSF model through the combination of an analytic Gaussian likelihood and a highly expressive generative diffusion model prior, trained on a library of HST ePSF templates. Compared to traditional methods (parametric Moffat, ePSF template-based, and regularized likelihood), we demonstrate that our PSF models achieve orders of magnitude higher likelihood and residuals consistent with noise, all while remaining visually realistic. Further, the method applies even for faint and heavily masked point sources, merely producing a broader posterior. By recovering a realistic, pixel-level posterior distribution, our technique enables the first meaningful propagation of detailed PSF morphological uncertainty in downstream analysis. An implementation of our posterior sampling procedure is available on GitHub.

2025-11-23

ArXiv (preprint)

Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models

Gabriel Missael Barco

2025-10-31

arXiv (published)

Bridging Simulators with Conditional Optimal Transport

Justine Zeghal

Benjamin Remy

François Lanusse

2025-10-27

ArXiv (preprint)

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images

Andreas Filipp

Tri Nguyen

J. Rose

Chris Lovell

Nicolas Payot

Francisco Villaescusa-navarro

2025-10-15

ArXiv (preprint)

The Interpolation Constraint in the RV Analysis of M-Dwarfs Using Empirical Templates

Dhvani Doshi

Nicolas B. Cowan

E. Artigau

René Doyon

André M. Silva

Khaled Al Moulla

Precise radial velocity (pRV) measurements of M-dwarfs in the near-infrared (NIR) rely on empirical templates due to the lack of accurate st… (see more)ellar spectral models in this regime. Templates are assumed to approximate the true spectrum when constructed from many observations or in the high signal-to-noise limit. We develop a numerical simulation that generates SPIRou-like pRV observations from PHOENIX spectra, constructs empirical templates, and estimates radial velocities. This simulation solely considers photon noise and evaluates when empirical templates remain reliable for pRV analysis. Our results reveal a previously unrecognized noise source in templates, establishing a fundamental floor for template-based pRV measurements. We find that templates inherently include distortions in stellar line shapes due to imperfect interpolation at the detector's sampling resolution. The magnitude of this interpolation error depends on sampling resolution and RV content. Consequently, while stars with a higher RV content, such as cooler M-dwarfs are expected to yield lower RV uncertainties, their dense spectral features can amplify interpolation errors, potentially biasing RV estimates. For a typical M4V star, SPIRou's spectral and sampling resolution imposes an RV uncertainty floor of 0.5-0.8 m/s, independent of the star's magnitude or the telescope's aperture. These findings reveal a limitation of template-based pRV methods, underscoring the need for improved spectral modeling and better-than-Nyquist detector sampling to reach the next level of RV precision.

2025-10-14

The Astronomical Journal (published)

caskade: building Pythonic scientific simulators

Alexandre Adam

Adam Coogan

2025-09-14

Journal of Open Source Software (published)

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… (see more)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 (published)

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

Maximiliano Isi

Kaze W. K. Wong

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from t… (see more)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 (published)

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 … (see 160 more)

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

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 (published)