Explainable machine learning workflows for radio astronomical data processing
This work addresses the need for interpretable automated data processing for astronomers, but it is incremental as it builds on existing ML methods with a focus on a specific domain application.
The paper tackled the problem of black-box machine learning pipelines in radio astronomy by proposing a hybrid approach using fuzzy rule-based inference and deep learning to improve explainability, demonstrating through simulations that it maintains quality and accuracy without compromising performance.
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.