Unveiling Hidden Clustering: An Unsupervised Machine Learning Study of Repeating FRB 20220912A

An-Chieh Hsu¹, Tetsuya Hashimoto^1*, Tomotsugu Goto², Tomoki Wada^1,5, Bjorn Jasper Raquel³, Simon C.-C. Ho⁴

¹Department of Physics, National Chung Hsing University, Taichung, Taiwan
²Institute of Astronomy, National Tsing Hua University, Hsinchu, Taiwan
³National Institute of Physics, University of the Philippines, Quezon, Philippines
⁴Research School of Astronomy and Astrophysics, The Australian National University, Canberra, Australia
⁵Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Japan

* Presenter:Tetsuya Hashimoto, email:tetsuya@phys.nchu.edu.tw

Fast Radio Bursts (FRBs) are millisecond-duration radio transients of extragalactic origin. Classifying repeating FRBs from a repeating source is essential for understanding their emission mechanisms, but remains challenging due to their short durations, high variability, and increasing data volume. Traditional methods often rely on subjective criteria and struggle with high-dimensional data. In this study, we apply an unsupervised machine learning framework—combining Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN)—to eight observed parameters from FRB 20220912A. Our analysis reveals three distinct clusters of bursts with varying spectral and fluence properties. Comparisons with clustering studies on other repeaters, such as FRB 20201124A and FRB121102, show that some of our clusters share similar features, suggesting possible common emission mechanisms. We also provide qualitative interpretations for each cluster, highlighting the spectral diversity within a single source. Notably, one cluster shows broadband emission and high fluence, typically seen in non-repeating FRBs, raising the possibility that some non-repeaters may be misclassified repeaters due to observational limitations. Our results demonstrate the utility of machine learning in uncovering intrinsic diversity in FRB emission and provide a foundation for future classification studies.

Keywords: Fast radio bursts, Radio astronomy, Machine learning