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A Topological Data Analysis of the CHIME/FRB Catalogues
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In this paper, we use Topological Data Analysis (TDA), a mathematical approach for studying data shape, to analyse Fast Radio Bursts (FRBs). Applying the Mapper algorithm, we visualise the topological structure of a large FRB sample. Our findings reveal three distinct FRB populations based on their inferred source properties, and show a robust structure indicating their morphology and energy. We also identify potential non-repeating FRBs that might become repeaters based on proximity in the Mapper graph. This work showcases TDA's promise in unraveling the origin and nature of FRBs.
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Cited by 2 Pith papers
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Comparative analysis of machine learning techniques for feature selection and classification of Fast Radio Bursts
Unsupervised clustering of CHIME Fast Radio Bursts recovered four of six sources reclassified as repeaters in the 2023 catalog, while derived features changed results unevenly across methods.
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Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations
Unsupervised clustering of 739 CHIME FRBs separates repeaters from non-repeaters, flags over 100 potential repeater candidates, and finds cluster-specific correlations among scattering time, burst width, brightness te...
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