Decoding the Radio Sky: Bayesian Ensemble Learning and SVD-Based Feature Extraction for Automated Radio Galaxy Classification

TL;DR

This paper introduces a probabilistic machine learning framework combining SVD feature extraction and Bayesian ensemble learning to accurately classify radio galaxies, improving scalability, interpretability, and uncertainty quantification in large radio survey datasets.

Contribution

It presents a novel integration of SVD and Bayesian ensemble methods for radio galaxy classification, enhancing accuracy and interpretability over traditional techniques.

Findings

Bayesian ensembles outperform traditional classifiers in accuracy and F1-score.

The framework achieves 99.0% accuracy in classifying multiple galaxy types.

SHAP analysis reveals key features associated with morphological differences.

Abstract

The classification of radio galaxies is central to understanding galaxy evolution, active galactic nuclei dynamics, and the large-scale structure of the universe. However, traditional manual techniques are inadequate for processing the massive, heterogeneous datasets generated by modern radio surveys. In this study, we present a probabilistic machine learning framework that integrates Singular Value Decomposition (SVD) for feature extraction with Bayesian ensemble learning to achieve robust, scalable radio galaxy classification. The SVD approach effectively reduces dimensionality while preserving key morphological structures, enabling efficient representation of galaxy features. To mitigate class imbalance and avoid the introduction of artefacts, we incorporate a Local Neighbourhood Encoding strategy tailored to the astrophysical distribution of galaxy types. The resulting features are used to train and optimize several baseline classifiers: Logistic Regression, Support Vector Machines, LightGBM, and Multi-Layer Perceptrons within bagging, boosting, and stacking ensembles governed by a Bayesian weighting scheme. Our results demonstrate that Bayesian ensembles outperform their traditional counterparts across all metrics, with the Bayesian stacking model achieving a classification accuracy of 99.0% and an F1-score of 0.99 across Compact, Bent, Fanaroff-Riley Type I (FR-I), and Type II (FR-II) sources. Interpretability is enhanced through SHAP analysis, which highlights the principal components most associated with morphological distinctions. Beyond improving classification performance, our framework facilitates uncertainty quantification, paving the way for more reliable integration into next-generation survey pipelines. This work contributes a reproducible and interpretable methodology for automated galaxy classification in the era of data-intensive radio astronomy.