Machine Learning Classification of Repeating FRBs from FRB121102

TL;DR

This study applies unsupervised machine learning to classify 1652 repeating FRBs from FRB121102, revealing four distinct clusters with sub-clusters, offering a more detailed understanding of their physical diversity.

Contribution

The paper introduces a comprehensive clustering approach using multiple physical parameters, identifying four clusters instead of the traditional two, enhancing the understanding of FRB subtypes.

Findings

Four clusters identified, including three sub-clusters within the 'Atypical' group.

Clustering results align with previous literature but provide more detailed classification.

Method demonstrates a holistic approach to FRB classification using physical parameters.

Abstract

Fast Radio Bursts (FRBs) are mysterious bursts in the millisecond timescale at radio wavelengths. Currently, there is little understanding about the classification of repeating FRBs, based on difference in physics, which is of great importance in understanding their origin. Recent works from the literature focus on using specific parameters to classify FRBs to draw inferences on the possible physical mechanisms or properties of these FRB subtypes. In this study, we use publicly available 1652 repeating FRBs from FRB121102 detected with the Five-hundred-meter Aperture Spherical Telescope (FAST), and studied them with an unsupervised machine learning model. By fine-tuning the hyperparameters of the model, we found that there is an indication for four clusters from the bursts of FRB121102 instead of the two clusters ("Classical" and "Atypical") suggested in the literature. Wherein, the "Atypical" cluster can be further classified into three sub-clusters with distinct characteristics. Our findings show that the clustering result we obtained is more comprehensive not only because our study produced results which are consistent with those in the literature but also because our work uses more physical parameters to create these clusters. Overall, our methods and analyses produced a more holistic approach in clustering the repeating FRBs of FRB121102.