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 :

Google Scholar

Misha Barth

PhD - Université de Montréal

Co-supervisor :

Noé Dia

Research Intern - Université de Montréal

Principal supervisor :

Master's Research - McGill University

PhD - Université de Montréal

Principal supervisor :

Ronan Legin

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 :

Google Scholar

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

Ronan Legin

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

Ronan Legin

2025-10-31

arXiv (published)

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images

Tri Nguyen

J. Rose

Chris Lovell

Nicolas Payot

Francisco Villaescusa-navarro

Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. … (see more)However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differences in the properties and environment of individual galaxies. We present a machine learning framework to infer the galaxy-specific predicted SHMF from galaxy images, conditioned on the assumed inverse warm DM particle mass

2025-10-16

ArXiv (preprint)

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images

Tri Nguyen

J. Rose

Chris Lovell

Nicolas Payot

Francisco Villaescusa-navarro

2025-10-16

ArXiv (preprint)

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

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.

2025-10-15

The Astronomical Journal (published)

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

Nicolas B. Cowan

Étienne Artigau

René Doyon

André M. Silva

Khaled Al Moulla

2025-10-15

The Astronomical Journal (published)

caskade: building Pythonic scientific simulators

Alexandre Adam

Adam Coogan

2025-09-15

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

2025-08-20

The Astrophysical Journal (published)

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

Nicolas B. Cowan

E. Artigau

René Doyon

Andr'e M. Silva

K. Al Moulla

Precise radial velocity (pRV) measurements of M dwarfs in the near-infrared rely on empirical templates due to the lack of accurate stellar … (see more)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 SpectroPolarimètre InfraRouge (SPIRou)-like pRV observations from PHOENIX spectra, constructs empirical templates, and estimates radial velocities (RVs). 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 created from stacking registered observations, establishing a noise floor for such template-based pRV measurements. We find that these 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, for stars with higher RV content, such as cooler M dwarfs, interpolation noise has a larger relative impact, making their performance comparable to hotter M dwarfs when using detectors with low sampling. For a typical M4V star, SPIRou’s spectral and sampling resolution imposes an RV uncertainty floor of 0.5–0.8 m s−1, 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.

2025-08-14

ArXiv (preprint)