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Ronan Legin

PhD - Université de Montréal


Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Alexandre Adam
Connor Stone
Connor Bottrell
Ronan Legin
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (see more) barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.
Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise
Ronan Legin
Alexandre Adam
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.
Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models
Ronan Legin
Matthew Ho
Pablo Lemos
Shirley Ho
Benjamin Wandelt
Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise
Ronan Legin
Alexandre Adam
Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy
Justin Spilker
Kedar A. Phadke
Manuel Aravena
Melanie Archipley
Matthew Bayliss
Jack E. Birkin
Matthieu Béthermin
James R. Burgoyne
Jared Cathey
Scott Chapman
Håkon Dahle
Anthony H. Gonzalez
Gayathri Gururajan
Christopher C Hayward
Ryley Hill
Taylor A. Hutchison
Keunho J. Kim
Seonwoo Kim
D. Law … (see 19 more)
Ronan Legin
M. Malkan
Daniel P. Marrone
E. Murphy
Desika Narayanan
Alexander Navarre
Grace M. Olivier
J. Rich
Jane R Rigby
Cassie Reuter
J. Rhoads
Keren Sharon
J. Smith
Manuel Solimano
Nikolaus Sulzenauer
Joaquin Vieira
David Vizgan
Axel Weiß
K. Whitaker
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 macroparameters of strong lensing systems with neural ne… (see more)tworks. 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, obtain accurate posteriors, and 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 1.4% at the 21.8% confidence level, without the need for any added calibration procedure. In total, inferring 100,000 different posteriors takes a day on a single GPU, showing that the method scales well to the thousands of lenses expected to be discovered by upcoming sky surveys.