# Explainable machine learning workflows for radio astronomical data processing

arXiv: https://arxiv.org/abs/2603.16350 · HTML: https://mlsanity.com/paper/2603.16350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions](https://mlsanity.com/paper/2102.04271/) (2021)
- [Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System](https://mlsanity.com/paper/2010.04974/) (2020)
- [PyTSK: A Python Toolbox for TSK Fuzzy Systems](https://mlsanity.com/paper/2206.03310/) (2022)
- [Hint assisted reinforcement learning: an application in radio astronomy](https://mlsanity.com/paper/2301.03933/) (2023)

## Abstract (arXiv)

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.