Explainable machine learning workflows for radio astronomical data processing

TL;DR

This paper introduces an explainable machine learning approach combining fuzzy rule-based inference and deep learning to improve transparency in radio astronomy data processing pipelines, specifically for calibration tasks.

Contribution

It proposes a novel hybrid ML framework using TSK fuzzy systems and deep learning to enhance explainability without sacrificing accuracy in radio astronomical data calibration.

Findings

Increased explainability demonstrated through simulations.

Maintained data processing accuracy with the new approach.

Applicable to calibration in radio astronomy.

Abstract

Radio astronomy relies heavily on efficient and accurate processing pipelines to deliver science ready data. With the increasing data flow of modern radio telescopes, manual configuration of such data processing pipelines is infeasible. Machine learning (ML) is already emerging as a viable solution for automating data processing pipelines. However, almost all existing ML enabled pipelines are of black-box type, where the decisions made by the automating agents are not easily deciphered by astronomers. In order to improve the explainability of the ML aided data processing pipelines in radio astronomy, we propose the joint use of fuzzy rule based inference and deep learning. We consider one application in radio astronomy, i.e., calibration, to showcase the proposed approach of ML aided decision making using a Takagi-Sugeno-Kang (TSK) fuzzy system. We provide results based on simulations to illustrate the increased explainability of the proposed approach, not compromising on the quality or accuracy.