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arxiv: 2312.14133 · v2 · pith:VYSX6N66 · submitted 2023-12-21 · astro-ph.HE

Morphologies of Bright Complex Fast Radio Bursts with CHIME/FRB Voltage Data

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classification astro-ph.HE
keywords driftingburstschimeeventspolarizationarchetypescharacteristicscomplex
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We present the discovery of twelve thus far non-repeating fast radio burst (FRB) sources, detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope. These sources were selected from a database comprising of order $10^3$ CHIME/FRB full-array raw voltage data recordings, based on their exceptionally high brightness and complex morphology. Our study examines the time-frequency characteristics of these bursts, including drifting, microstructure, and periodicities. The events in this sample display a variety of unique drifting phenomenologies that deviate from the linear negative drifting phenomenon seen in many repeating FRBs, and motivate a possible new framework for classifying drifting archetypes. Additionally, we detect microstructure features of duration $\lesssim$ 50 $\mu s$ in seven events, with some as narrow as $\approx$ 7 $\mu s$. We find no evidence of significant periodicities. Furthermore, we report the polarization characteristics of seven events, including their polarization fractions and Faraday rotation measures (RMs). The observed $|\mathrm{RM}|$ values span a wide range of $17.24(2)$ - $328.06(2) \mathrm{~rad~m}^{-2}$, with linear polarization fractions between $0.340(1)$ - $0.946(3)$. The morphological properties of the bursts in our sample appear broadly consistent with predictions from both relativistic shock and magnetospheric models of FRB emission, as well as propagation through discrete ionized plasma structures. We address these models and discuss how they can be tested using our improved understanding of morphological archetypes.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Frabjous: Deep Learning Fast Radio Burst Morphologies

    astro-ph.IM 2025-07 unverdicted novelty 4.0

    Frabjous applies deep learning to classify FRB morphologies into five classes at 55% accuracy by augmenting limited real data with simulations.