Revisiting the Mysterious Origin of FRB 20121102A with Machine-learning Classification

Aug 4, 2026

Fast radio bursts (FRBs) are millisecond-duration radio emissions whose origin and physical emission mechanisms remain unresolved despite the proposal of over 50 models. Classification is a key route to uncovering these mechanisms, yet prior approaches often rely on a limited set of observational parameters. This work applies unsupervised machine learning using UMAP to jointly analyze seven burst parameters and cluster 977 homogeneous sub-bursts of FRB 20121102A from Arecibo. The resulting five clusters suggest multiple physical mechanisms and may be influenced by emission geometry and propagation effects, providing a benchmark for future large-sample surveys.

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Introduction

arXiv :2410 .00576v1 [ astro-ph .HE] 1 Oct 2024 Revisiting the Mysterious Origin of FRB 20121102A with Machine-learning Classification Leah Ya-Ling Lin,1 Tetsuya Hashimoto,2 Tomotsugu Goto,1,3 Bjorn Jasper Raquel,2,4 Simon C.-C. Ho,5,6,7,8 Bo-Han Chen,9 Seong Jin Kim,1,3 and Chih-Teng Ling3 1 Department of Physics, National Tsing Hua University, 101, Section 2 . Kuang-Fu Road, Hsinchu, 30013, Taiwan 2 Department of Physics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung 40227, Taiwan 3 Institute of Astronomy, National Tsing Hua University, 101, Section 2 . Kuang-Fu Road, Hsinchu, 30013, Taiwan 4 Department of Earth and Space Sciences, Rizal Technological University, Boni Avenue, Mandaluyong, 1550 Metro Manila, Philippines 5 Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611, Australia 6 Centre for Astrophysics and Supercomputing, Swinburne University of Technology, P.O. Box 218, Hawthorn, VIC 3122, Australia 7 OzGrav: The Australian Research Council Centre of Excellence for Gravitational Wave Discovery, Hawthorn, VIC 3122, Australia 8ASTRO3D: ARC Centre of Excellence for All-sky Astrophysics in 3D, ACT 2611, Australia 9 Graduate School of Data Science, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea Author for correspondence: Leah Ya-Ling Lin, Email: [stu109022104@gapp.nthu.edu.tw](stu109022104@gapp.nthu.edu.tw). Abstract Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection (UMAP) to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts ofFRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect ofFRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the Square Kilometer Array (SKA) or Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) discover more FRBs than before. Keywords: radio continuum: galaxies – methods: data – methods: numerical – methods: analytical 1. Introduction Fast radio bursts (FRBs) are a type of highly energetic astrophysical transient that last only a few milliseconds (e.g., Lorimer et al. 2007) . Many FRBs have dispersion measures (DMs) that exceed the expected maximum of the Galactic electron density, indicating their extragalactic origins. DM represents the column density of free electrons traversed along the propagation path of an FRB. Despite their discovery over a decade ago (Lorimer et al. 2007), the origin ofFRBs remains a mystery. Recently, the detection of repeating FRBs (e.g., Spitler et al. 2014; Niu et al. 2022) has opened up new avenues of research into the origin of these phenomena. With the emergence of a large number ofFRBs samples in recent years, repeated FRBs (referred to as ‘repeating bursts’for simplicity) have also been noticed by astronomers, especially FRB 20121102A, which has been observed to have avery high burst rate (e.g., Li et al. 2021; Jahns et al. 2022) . FRB 20121102A is the first-discovered repeating FRB source (Sc

FAQs

Why is the origin of fast radio bursts (FRBs) still considered unknown?

More than 50 physical models have been proposed, but the physical mechanism and origin of FRBs remain unresolved, even though many extragalactic indications come from dispersion measures.

What problem do earlier FRB classification studies face?

Earlier work often classified FRBs using only a few observational parameters, such as fluence and duration, which may be incomplete for capturing the full diversity of burst properties.

How does this study classify FRBs and what does it find for FRB 20121102A?

It uses an unsupervised machine-learning model (UMAP) to jointly handle seven parameters for 977 sub-bursts from Arecibo. The analysis identifies five distinct clusters, suggesting multiple possible physical mechanisms and potentially the influence of emission geometry and propagation effects.

Aug 4, 2026

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