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 :

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 :

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Publications

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

Pablo Lemos

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.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (published)

proceedings.mlr.press

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

Alexandre Adam

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)

Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with non-Gaussian Noise

Ronan Legin

Alexandre Adam

Laurence Perreault Levasseur

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 is a major limitation for precision measurements in many domains of science, including astrophysics, for example, for the analysis of the Cosmic Microwave Background, gravitational waves, gravitational lensing, and exoplanets. 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.

2023-05-31

The Astrophysical Journal Letters (published)

Spectroscopy of CASSOWARY gravitationally-lensed galaxies in SDSS: characterisation of an extremely bright reionization-era analog at z = 1.42

Ramesh Mainali

Daniel P Stark

Tucker Jones

Richard S Ellis

Jane R Rigby

We present new observations of sixteen bright (r = 19 − 21) gravitationally lensed galaxies at z ≃ 1 − 3 selected from the CASSOWARY s… (see more)urvey. Included in our sample is the z = 1.42 galaxy CSWA-141, one of the brightest known reionization-era analogs at high redshift (g=20.5), with a large sSFR (31.2 Gyr−1) and an [OIII]+Hβ equivalent width (EW[OIII] + Hβ=730 Å) that is nearly identical to the average value expected at z ≃ 7 − 8. In this paper, we investigate the rest-frame UV nebular line emission in our sample with the goal of understanding the factors that regulate strong CIII] emission. Whereas most of the sources in our sample show weak UV line emission, we find elevated CIII] in the spectrum of CSWA-141 (EWCIII]=4.6±1.9 Å) together with detections of other prominent emission lines (OIII], Si III], Fe II⋆, Mg II). We compare the rest-optical line properties of high redshift galaxies with strong and weak CIII] emission, and find that systems with the strongest UV line emission tend to have young stellar populations and nebular gas that is moderately metal-poor and highly ionized, consistent with trends seen at low and high redshift. The brightness of CSWA-141 enables detailed investigation of the extreme emission line galaxies which become common at z > 6. We find that gas traced by the CIII] doublet likely probes higher densities than that traced by [OII] and [SII]. Characterisation of the spectrally resolved Mg II emission line and several low ionization absorption lines suggests neutral gas around the young stars is likely optically thin, potentially facilitating the escape of ionizing radiation.

2023-02-07

Monthly Notices of the Royal Astronomical Society (published)

A Framework for Obtaining Accurate Posteriors of Strong Gravitational Lensing Parameters with Flexible Priors and Implicit Likelihoods using Density Estimation

Ronan Legin

Benjamin Wandelt

We report the application of implicit likelihood inference to the prediction of the macro-parameters of strong lensing systems with neural n… (see more)etworks. This allows us to perform deep learning analysis of lensing systems within a well-defined Bayesian statistical framework to explicitly impose desired priors on lensing variables, to obtain accurate posteriors, and to guarantee convergence to the optimal posterior in the limit of perfect performance. We train neural networks to perform a regression task to produce point estimates of lensing parameters. We then interpret these estimates as compressed statistics in our inference setup and model their likelihood function using mixture density networks. We compare our results with those of approximate Bayesian neural networks, discuss their significance, and point to future directions. Based on a test set of 100,000 strong lensing simulations, our amortized model produces accurate posteriors for any arbitrary confidence interval, with a maximum percentage deviation of

2023-01-18

The Astrophysical Journal (published)

Posterior samples of source galaxies in strong gravitational lenses with score-based priors

Yoshua Bengio

Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge,… (see more) in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces independent posterior samples and models the data almost down to the noise level. We show how the balance between the likelihood and the prior meet our expectations in an experiment with out-of-distribution data.

2022-11-06

ArXiv (preprint)

Machine Learning Advantages in Canadian Astrophysics

Kim Venn

Sébastien Fabbro

Adrian Liu

Gwendolyn Eadie

Sara Ellison

Joanna Woo

JJ Kavelaars

Kwang Moo Yi

Renée Hložek

Jo Bovy

Hossen Teimoorinia

Siamak Ravanbakhsh

Locke Spencer

The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing pow… (see more)er and complex algorithms become more powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010.
With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers.

2019-10-01

ArXiv (preprint)