Hunting for the candidates of Changing-Look Blazar using Mclust Clustering Analysis
Shi-Ju Kang, Shan-Shan Ren, Yong-Gang Zheng, and Qingwen Wu

TL;DR
This study uses Gaussian Mixture Modeling to identify 111 candidate changing-look blazars from a large dataset, including 67 newly proposed candidates, enhancing understanding of blazar state transitions.
Contribution
It applies Mclust clustering analysis to classify blazars and identify new changing-look blazar candidates, expanding the known sample and insights into their physical properties.
Findings
Identified 111 candidate changing-look blazars, including 67 new candidates.
Found 4 data subsets with high clustering consistency (ARI > 0.610).
Confirmed 44 previously known CLBs among the candidates.
Abstract
The changing-look blazars (CLBs) are the blazars that their optical spectral lines at different epochs show a significant changes and present a clear transition between the standard FSRQ and BL Lac types. The changing-look phenomena in blazars are highly significant for enhancing our understanding of certain physical problems of active galactic nuclei (AGNs), such as the potential mechanism of the state transition in the accretion process of the supermassive black holes in the central engine of AGNs, the possible intrinsic variation of the jet, and the connection between the accretion disk and the jet. Currently, the CLBs reported in the literature are still rare astronomical objects. In our previous work, we found that there are 8 physical properties parameters of CLBs located between those of FSRQs and those of BL Lacs. In order to search more CLB candidates (CLBCs), we employed the…
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Taxonomy
TopicsBig Data Technologies and Applications · Computational Physics and Python Applications
