论文标题
机器学习应用于天体物理学的多频数据：Blazar分类
Machine Learning applied to Multifrequency Data in Astrophysics: Blazar Classification
论文作者
论文摘要
对天体物理来源自主分类的机器学习（ML）技术的研究引起了人们的极大兴趣，我们在多频数据框架的背景下探索了其应用。我们测试了监督ML根据其同步峰频率对Blazar进行分类的使用，该峰值低于或高于10 $^{15} $ Hz。我们选择一个标记为1279高同步峰（HSP：$ \rmν$ -peak> 10 $^{15} $ Hz）和2899低同步器峰（LSP：lsp：$ \ rmν$ -peak <10 $^15} $ hz）的样品。定义了一组多频功能来表示每个源，其中包括光谱斜率（$α_{ν_1，ν_2} $），也考虑了IR颜色，也考虑了无线电，Infra-Red，Optical和X射线频段之间的光谱斜率。我们描述了将BLAZAR分类为LSP或HSP的五种ML分类算法的优化：随机森林（RF），支持向量机（SVM），K-Nearest邻居（KNN），高斯幼稚的贝叶斯（GNB）和Ludwig Auto-ML框架。在我们的特殊情况下，SVM算法的性能最佳，达到了平衡准确性的93％。联合功能置换测试表明，光谱斜率alpha-radio-ir和alpha-radio-Optical与ML建模最相关，其次是IR颜色。这项工作表明，ML算法可以区分多频谱特征，并将Blazars的分类处理为LSP和HSP。这是在自动确定宽带光谱参数（作为同步子$ν$ -PEAK）的自动确定，甚至可以在全高数据库中搜索新的Blazars。
The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML to classify blazars according to its synchrotron peak frequency, either lower or higher than 10$^{15}$Hz. We select a sample with 4178 blazars labelled as 1279 high synchrotron peak (HSP: $\rm ν$-peak > 10$^{15}$Hz) and 2899 low synchrotron peak (LSP: $\rm ν$-peak < 10$^{15}$Hz). A set of multifrequency features were defined to represent each source, that includes spectral slopes ($α_{ν_1, ν_2}$) between the radio, infra-red, optical, and X-ray bands, also considering IR colours. We describe the optimisation of five ML classification algorithms that classify blazars into LSP or HSP: Random Forests (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gaussian Naive Bayes (GNB) and the Ludwig auto-ML framework. In our particular case, the SVM algorithm had the best performance, reaching 93% of balanced-accuracy. A joint-feature permutation test revealed that the spectral slopes alpha-radio-IR and alpha-radio-optical are the most relevant for the ML modelling, followed by the IR colours. This work shows that ML algorithms can distinguish multifrequency spectral characteristics and handle the classification of blazars into LSPs and HSPs. It is a hint for the potential use of ML for the autonomous determination of broadband spectral parameters (as the synchrotron $ν$-peak), or even to search for new blazars in all-sky databases.
