Yashar Hezaveh

Google Scholar

Biographie

Yashar Hezaveh est membre associé de Mila – Institut québécois d'intelligence artificielle et directeur de Ciela – Institut de Montréal pour l'analyse des données astrophysiques et l'apprentissage automatique. Il est professeur adjoint au Département de physique de l'Université de Montréal, titulaire d'une chaire de recherche du Canada en analyse de données astrophysiques et apprentissage automatique, membre associé de l'Institut spatial Trottier de l'Université McGill et chercheur invité au Center for Computational Astrophysics du Flatiron Institute (New York) et au Perimeter Institute. Auparavant, il a été chercheur au Flatiron Institute (2018-2019) et boursier Hubble de la NASA à l'Université de Stanford (2013-2018).

Il est un leader mondial dans l'analyse des données astrophysiques avec l'apprentissage automatique. Ses recherches actuelles portent principalement sur l'inférence bayésienne dans l'IA (par exemple, les modèles de diffusion) et visent à faire progresser les connaissances sur la distribution de la matière noire dans les galaxies fortement lenticulaires à l'aide de données provenant de grands relevés cosmologiques. Ses recherches sont soutenues par la Schmidt Futures Foundation et la Simons Foundation.

Étudiants actuels

Missael Barco

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Google Scholar

Misha Barth

Doctorat - UdeM

Co-superviseur⋅e :

Noé Dia

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - McGill

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Ronan Legin

Doctorat - UdeM

Co-superviseur⋅e :

Pablo Lemos

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Pierre-Luc Bacon

Nicolas Payot

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Nolan Smyth

Postdoctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - McGill

Yitian Sun

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Wassim Tenachi

Postdoctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Google Scholar

Publications

Transformer Embeddings for Fast Microlensing Inference

Nolan Smyth

2025-12-11

ArXiv (prépublication)

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… (voir plus) 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 (prépublication)

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 (prépublication)

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… (voir plus)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 (prépublication)

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 (publié)

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. … (voir plus)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 (prépublication)

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 (prépublication)

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 (publié)

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… (voir plus)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 (publié)

caskade: building Pythonic scientific simulators

Alexandre Adam

Adam Coogan

2025-09-15

Journal of Open Source Software (publié)

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 (publié)

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 … (voir plus)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 (prépublication)