Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry

TL;DR

This paper introduces an unsupervised, conformalized bootstrap method to quantify uncertainties in fast radio interferometric image reconstructions, enhancing reliability for next-generation radio astronomy data analysis.

Contribution

It develops a novel uncertainty quantification technique tailored for ultra-fast unrolled algorithms in radio interferometry, improving reliability over existing methods.

Findings

Provides more trustworthy uncertainty estimates for fast reconstruction algorithms.

Demonstrates applicability to large-scale data from next-generation radio telescopes.

Enhances scientific interpretation of radio astronomical observations.

Abstract

The advent of next-generation radio interferometers like the Square Kilometer Array promises to revolutionise our radio astronomy observational capabilities. The unprecedented volume of data these devices generate requires fast and accurate image reconstruction algorithms to solve the ill-posed radio interferometric imaging problem. Most state-of-the-art reconstruction methods lack trustworthy and scalable uncertainty quantification, which is critical for the rigorous scientific interpretation of radio observations. We propose an unsupervised technique based on a conformalized version of a radio-augmented equivariant bootstrapping method, which allows us to quantify uncertainties for fast reconstruction methods. Noticeably, we rely on reconstructions from ultra-fast unrolled algorithms. The proposed method brings more reliable uncertainty estimations to our problem than existing alternatives.