Classification of Pulsar Glitch Amplitudes using Extreme Deconvolution

TL;DR

This paper classifies pulsar glitch amplitudes using an advanced statistical method that accounts for measurement uncertainties, revealing a bimodal distribution and providing insights into pulsar glitch behaviors.

Contribution

It introduces the application of Extreme Deconvolution with Gaussian Mixture Models to classify pulsar glitch amplitudes considering uncertainties, and determines the optimal number of classes using information criteria.

Findings

Glitch amplitudes are best described by a bimodal distribution.

Two distinct groups of glitch amplitudes with different mean values.

Inter-glitch time intervals show different preferred classifications depending on the criterion.

Abstract

We carry out a classification of the glitch amplitudes of radio pulsars using Extreme Deconvolution technique based on the Gaussian Mixture Model, where the observed uncertainties in the glitch amplitudes $\Delta \nu/\nu$ are taken into account. Our dataset consists of 699 glitches from 238 pulsars. We then use information theory criteria such as AIC and BIC to determine the optimum number of glitch classes. We find that both AIC and BIC show that the pulsar glitch amplitudes can be optimally described using a bimodal distribution. The mean values of $\Delta \nu/\nu$ for the two components are equal to $4.79 \times 10^{-9}$ and $1.28 \times 10^{-6}$, respectively with standard deviation given by 1.01 and 0.55 dex. We also applied this method to classify the pulsar inter-glitch time intervals, and we find that AIC prefers two components, whereas BIC prefers a single component. The unified data set and analyses codes used in this work have been made publicly available.