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.

我们将观测到的脉冲星数据集分类为不同的簇，基于 $磷-\dot{磷}$图，使用基于极端反卷积的高斯混合模型。然后我们使用贝叶斯信息准则来选择最佳簇数。我们发现与之前的工作一致，脉冲星数据集可以最佳地分为六个集群，其中两个用于毫秒脉冲星种群，四个用于普通脉冲星种群。然而，除此之外，我们没有收集到基于这种分类的脉冲星种群的任何额外见解。使用数值实验，我们确认与普通高斯混合模型相比，基于极限反卷积的分类对数据集中的变化不太敏感。我们用于这项工作的所有分析代码都已公开。