Explainable machine learning workflows for radio astronomical data processing

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.

Figures (4)

• Figure 1: A geometrical overview of the observing setup. While a target area in the sky is being observed, several strong outlier sources act as interference.
• Figure 2: The statistical spread of the simulated data used in training the ML model: (a) elevation (b) azimuth (c) separation.
• Figure 3: The learned Gaussian membership functions for (a) elevation (b) azimuth (c) separation. Looking at their complexity, we see that the azimuth plays an equally important role as the elevation in making a correct choice (unlike the separation, which is probably dependent on the azimuth and elevation).
• Figure 4: The training losses and the performance of the trained ML model compared with a purely data driven approach. (a) training/testing losses (b) reward (negative AIC) evaluation of the trained ML models compared to the data-driven approach.