Cluster Analysis of the Roma-BZCAT Blazars

作     者：Dmitry O.Kudryavtsev Yulia V.Sotnikova Vladislav A.Stolyarov Timur V.Mufakharov Valery V.Vlasyuk Margarita L.Khabibullina Alexander G.Mikhailov Yulia V.Cherepkova 

作者机构：Special Astrophysical Observatory of the Russian Academy of SciencesNizhny Arkhyz 369167Russia Astrophysics GroupCavendish LaboratoryUniversity of CambridgeCambridge CB3 OHEUK Kazan Federal University18 Kremlyovskaya StKazan 420008Russia 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2024年第24卷第5期

页      面：117-139页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

基　　金：National Aeronautics and Space Administration, NASA SED Builder Foundation for Ichthyosis and Related Skin Types, FIRST IPAC Ministry of Education and Science of the Russian Federation, Minobrnauka, (075-15-2022-1227) Ministry of Education and Science of the Russian Federation, Minobrnauka Sloan Digital Sky Survey, (2MASS) Panoramic Survey Telescope and Rapid Response System, (RATAN-600) 

主　　题：methods data analysis-galaxies active-(galaxies:)BL Lacertae objects general 

摘      要：Based on the collected multiwavelength data, namely in the radio(NVSS, FIRST, RATAN-600), IR(WISE),optical(Pan-STARRS), UV(GALEX), and X-ray(ROSAT, Swift-XRT) ranges, we have performed a cluster analysis for the blazars of the Roma-BZCAT catalog. Using two machine learning methods, namely a combination of PCA with k-means clustering and Kohonen s self-organizing maps(SOMs), we have constructed an independent classification of the blazars(five classes) and compared the classes with the known Roma-BZCAT classification(FSRQs, BL Lacs, galaxy-dominated BL Lacs, and blazars of an uncertain type) as well as with the high synchrotron peaked(HSP) blazars from the 3HSP catalog and blazars from the TeVCat catalog. The obtained groups demonstrate concordance with the BL Lac/FSRQ classification along with a continuous character of the change in the properties. The group of HSP blazars stands out against the overall distribution. We examine the characteristics of the five groups and demonstrate distinctions in their spectral energy distribution shapes. The effectiveness of the clustering technique for objective analysis of multiparametric arrays of experimental data is demonstrated.