Explainable machine learning workflows for radio astronomical data processing

S. Yatawatta; A. Ahmadi; B. Asabere; M. Iacobelli; N. Peters; M. Veldhuis

astro-ph.IMMar 17, 2026

Explainable machine learning workflows for radio astronomical data processing

Authors: S. Yatawatta, A. Ahmadi, B. Asabere, M. Iacobelli, N. Peters, M. Veldhuis

Organizations: ASTRON, The Netherlands Institute for Radio Astronomy, Dwingeloo, The Netherlands

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 & tables

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.

Outliers	CasA, CygA, TauA,
	VirA, HerA
Number of outliers $K$	5
Observation duration	30 s
Number of subbands	3
Frequency range	$[15,70]$ or $[110,180]$ MHz
Stations $N$	46 (LBA) or 62 (HBA)

Table 1: The simulation and deep learning settings

Panel of Figure 2 — Figure 2: The statistical spread of the simulated data used in training the ML model: (a) elevation (b) azimuth (c) separation.

Panel of Figure 3 — 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).

Panel of Figure 4 — 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.

Radio Astronomy

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Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

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Dept. of Astronomy & the DiRAC Institute, University of Washington, Box 351580, Seattle, WA 98195, USA · eScience Institute, University of Washington, Seattle, WA, U.S.A. · School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA+6

Scientific ML Astrophysics

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The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that increasingly shift the bottleneck in astronomical machine learning (ML) projects from model design to infrastructure. We present Hyrax, an open-source, modular, GPU-enabled Python framework that supports the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive two- and three-dimensional latent-space exploration for unsupervised discovery. We demonstrate Hyrax's versatility through five representative applications on real survey data: (i) unsupervised representation learning on

\sim 4\times10^5

Rubin Legacy Survey of Space and Time (LSST) Data Preview 1 (DP1) galaxies, surfacing new merger and low-surface-brightness candidates missing from reference Euclid and Dark Energy Survey catalogs, while also isolating imaging artifacts -- all without labeled training data; (ii) hybrid density-based clustering for identifying cluster-scale gravitational lens candidates in DP1 data; (iii) multimodal early-time transient classification in the Zwicky Transient Facility leveraging light curves, spectra, images, and metadata; (iv) supervised false-positive filtering in shift-and-stack searches for distant solar system objects in the Dark Energy Camera Ecliptic Exploration Project survey; and (v) supervised detection of semi-resolved dwarf galaxies in Hyper Suprime-Cam and LSST-like imaging using synthetic source injection. Together, these results demonstrate that Hyrax provides astronomy-specific ML infrastructure that enables systematic discovery and rapid methodological iteration across next-generation astronomical surveys.

Abstract

Radio Astronomy

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