Classification of Pulsars using Extreme Deconvolution

TL;DR

This paper applies Extreme Deconvolution Gaussian Mixture Models to classify pulsars into six clusters based on the $P-\, ext{dot} ext{-}P$ diagram, confirming previous classifications and demonstrating robustness over traditional methods.

Contribution

It introduces the use of Extreme Deconvolution for pulsar classification and compares its effectiveness to standard Gaussian Mixture Models.

Findings

Optimal classification into six clusters confirmed

Extreme Deconvolution is less sensitive to dataset variations

Analysis codes are publicly available

Abstract

We carry out a classification of the observed pulsar dataset into distinct clusters, based on the $P-\dot{P}$ 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.