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

TL;DR

This study employs unsupervised machine learning techniques to identify and analyze distinct clustering patterns in the repeating FRB 20220912A, revealing spectral diversity and potential links to other FRB sources, thereby advancing understanding of their emission mechanisms.

Contribution

Introduces a novel unsupervised clustering framework using UMAP and HDBSCAN to classify FRB bursts, uncovering intrinsic diversity and similarities with other repeaters.

Findings

Identified three distinct burst clusters with different spectral and fluence properties.

Found similarities between clusters in FRB 20220912A and other known repeaters.

Discovered a broadband, high-fluence cluster resembling non-repeating FRBs.

Abstract

Fast Radio Bursts (FRBs) are millisecond-duration radio transients of extragalactic origin. Classifying repeating FRBs 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 that combines 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 show that some of our clusters share similar features with sources such as FRB 20201124A and FRB 121102, 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, which are typically seen in non-repeating FRBs. This raises 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.