Explainable machine learning workflows for radio astronomical data processing
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...
| Authors | S. Yatawatta, A. Ahmadi, B. Asabere et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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