IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
This work addresses image reconstruction for radio astronomers, offering a novel Bayesian approach with expressive priors, though it is incremental as it builds on existing score-based models applied to a specific domain.
The authors tackled the challenge of reconstructing sky images from noisy radio interferometry data by introducing IRIS, a Bayesian method that uses score-based priors trained on optical galaxy images. They demonstrated that IRIS produces plausible posterior samples and evaluated its accuracy through coverage testing on simulations, showing advantages over traditional algorithms.
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.