论文标题
使用极端反卷积对脉冲星进行分类
Classification of Pulsars using Extreme Deconvolution
论文作者
论文摘要
我们使用基于极端反卷积的高斯混合物模型将观察到的脉冲星数据集分类为不同的簇。然后，我们使用贝叶斯信息标准选择最佳簇数。我们发现，根据以前的工作，PULSAR数据集可以最佳地分为六个群集，其中两个用于毫秒的脉冲星人口，四个用于普通的脉冲星人群。除此之外，但是，我们没有根据此分类获得对脉冲星人群的任何其他见解。使用数值实验，我们确认与普通的高斯混合模型相比，基于极端反卷积的分类对数据集的变化敏感。我们用于这项工作的所有分析代码均已公开可用。
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.
