Noé Dia

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e

Laurence Perreault-Levasseur

Co-supervisor

Yashar Hezaveh

Sujets de recherche

Apprentissage profond

Modèles génératifs

GitHub

Publications

IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Laurence Perreault-Levasseur

Yashar Hezaveh

Anna M. M. Scaife

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (voir plus)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.

2025-01-04

ArXiv (prépublication)

doi.org

arxiv.org

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Pablo Lemos

A. Scaife

Yashar Hezaveh