Joint deconvolution and blind source separation on the sphere with an application to radio-astronomy

TL;DR

This paper introduces SDecGMCA, a novel algorithm for joint deconvolution and blind source separation tailored for spherical data, enabling advanced analysis in radio-astronomy.

Contribution

The paper presents a new DBSS algorithm specifically designed for spherical data, addressing a gap in joint deconvolution and source separation methods.

Findings

Effective separation of sources in spherical radio-astronomy data

Handles large-field data analysis efficiently

Improves upon existing methods in accuracy and scalability

Abstract

Blind source separation is one of the major analysis tool to extract relevant information from multichannel data. While being central, joint deconvolution and blind source separation (DBSS) methods are scarce. To that purpose, a DBSS algorithm coined SDecGMCA is proposed. It is designed to process data sampled on the sphere, allowing large-field data analysis in radio-astronomy.