Classification of pulsars using Extreme Deconvolution

Reddy Ch., Tarun Tej and Desai, Shantanu (2022) Classification of pulsars using Extreme Deconvolution. New Astronomy, 91. p. 101673. ISSN 13841076

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We carry out a classification of the observed pulsar dataset into distinct clusters, based on the P−Ṗ diagram, using Extreme Deconvolution based Gaussian Mixture Model. We then use the Bayesian Information Criterion to select the optimum number of clusters. We find in accord with previous works, that the pulsar dataset can be optimally classified into six clusters, with two for the millisecond pulsar population, and four for the ordinary pulsar population. Beyond that, however we do not glean any additional insight into the pulsar population based on this classification. Using numerical experiments, we confirm that Extreme Deconvolution-based classification is less sensitive to variations in the dataset compared to ordinary Gaussian Mixture Models. All our analysis codes used for this work have been made publicly available.

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IITH Creators:
IITH CreatorsORCiD
Reddy Ch., Tarun TejUNSPECIFIED
Desai, ShantanuUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Classification; Extreme Deconvolution; Radio pulsars
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 06 Sep 2021 07:37
Last Modified: 06 Sep 2021 07:37
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