摘要
耀变体(Blazar)的光谱能量分布(Spectral Energy Distribution,SED)中显示有两个明显的峰,其中低能峰为同步辐射峰。耀变体同步辐射峰的峰值功率与峰值频率存在反相关关系,因此与光谱能量分布峰值有关的多波段复合光谱指数构建的谱指数平面可以作为耀变体的两个亚类(平谱射电类星体(Flat Spectrum Radio Quasar,FSRQ)和蝎虎座BL型天体(BL Lac))的分类器。通过斯特拉斯堡天文台的数据库为费米4期目录(4FGL-DR3)中的耀变体进行了多波段数据的交叉匹配,得到多波段数据样本。在此基础上发现,耀变体的8个宽波段谱指数(α_(rw1),α_(rw2),α_(rw3),αro,αrγ,α_(w1w2),α_(w1w3)和α_(w3x))分布具有双峰结构,且明显区分耀变体的两个亚类。通过机器学习中的支持向量机(Support Vector Machine,SVM)模型,进一步分析了谱指数平面对耀变体亚类的分类作用,发现大部分谱指数平面分类的准确率在80%以上。
There are two obvious peaks in the spectral energy distribution(SED)of the blazar,of which the low energy peak is the synchrotron radiation peak.There is an inverse correlation between the peak power of the synchrotron radiation peak of the blazar and the peak frequency,so the spectral index plane constructed by the multi-band composite spectral index related to the SED peak can be used as a classifier for the two subclasses of the blazar(flat-spectrum radio quasar(FSRQ)and BL-Lac object).In this paper,the cross matching of multi-wavelength data for the blazars in the Fermi four Catalogue(4FGL-DR3)is carried out through the database of Strasbourg Observatory,and multi-wavelength data set is obtained.Basing on this data set,we find that the distributions of the 8 broad band spectral indices,(α_(rw1),α_(rw2),α_(rw3),αro,αrγ,α_(w1w2),α_(w1w3))andα_(w3x),have obvious bimodal structure,and clearly distinguish the two subclasses of blazed variants(FSRQ and BL Lac).Through the support vector machine model in machine learning technology,we further analyze the classification effect of spectral index plane on subclasses of blazar,and find that most of the accuracy rates are above 80%.
作者
陈志晖
易庭丰
陈军平
王娜
普治元
张顺
Chen Zhihui;Yi Tingfeng;Chen Junping;Wang Na;Pu Zhiyuan;Zhang Shun(School of Physics and Electronic Information,Yunnan Normal University,Kunming 650500,China,Email:yitingfeng@ynnu.edu.cn;Guangxi Key Laboratory for Relativistic Astrophysics,Nanning 530004,China)
出处
《天文研究与技术》
CSCD
2023年第3期189-197,共9页
Astronomical Research & Technology
基金
国家自然科学基金(12063007,11863007)
国家天文台云南师范大学天文科普教育基地(04800205020503008)资助.
关键词
耀变体
宽波段谱指数
机器学习
分类方法
Blazar
broadband spectral indices
machine learning
classification method
