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.

2023-11-29

ArXiv (preprint)

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.

2023-11-03

NeurIPS.cc/2023/Workshop/Deep_Inverse (poster)

openreview.net

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

2023-10-13

Monthly Notices of the Royal Astronomical Society: Letters (published)

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

Ronan Legin

Alexandre Adam

2023-06-06

The Astrophysical Journal Letters (published)

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

2023-06-05

Nature (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 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.

2023-01-19

The Astrophysical Journal (published)