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

TL;DR

IRIS introduces a Bayesian method using score-based priors trained on optical galaxy images to improve radio interferometric image reconstruction, producing plausible and calibrated posterior samples from noisy data.

Contribution

This work presents IRIS, a novel Bayesian framework that leverages expressive score-based priors trained on optical images for radio interferometry image reconstruction.

Findings

Produces plausible posterior samples despite prior misspecification

Demonstrates improved image quality over traditional algorithms

Provides calibrated posterior samples through coverage testing

Abstract

Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge 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.