论文标题
使用极端反卷积的脉冲星故障振幅分类
Classification of Pulsar Glitch Amplitudes using Extreme Deconvolution
论文作者
论文摘要
我们使用基于高斯混合模型的极端反卷积技术对无线电脉冲星的故障振幅进行了分类，在该技术中，考虑到小故障振幅中观察到的不确定性$Δν/ν$。我们的数据集由238个脉冲星的699个故障组成。然后，我们使用信息理论标准（例如AIC和BIC）来确定故障类别的最佳数量。我们发现AIC和BIC都表明可以使用双峰分布最佳地描述脉冲星毛刺振幅。两个组件的$Δν/ν$的平均值等于$ 4.79 \ times 10^{ - 9} $和$ 1.28 \ times 10^{ - 6} $，分别为1.01和0.55 dex的标准偏差。我们还应用了此方法来对PULSAR间居间时间间隔进行分类，并且我们发现AIC更喜欢两个组件，而BIC更喜欢单个组件。这项工作中使用的统一数据集和分析代码已公开可用。
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 $Δν/ν$ 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 $Δν/ν$ 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.
