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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 1 Pith paper
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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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