The relativistic jet and its central engine of $Fermi$ blazars
Hubing Xiao, Zhihao OuYang, Lixia Zhang, Liping Fu, Shaohua Zhang,, Xiangtao Zeng, Junhui Fan

TL;DR
This study analyzes a large sample of Fermi blazars to understand jet origins, distinguishing BL Lacs and FSRQs, and proposes new empirical formulas and machine learning methods for classification, black hole mass estimation, and Doppler factor lower limits.
Contribution
It introduces a novel classification dividing line using machine learning, empirical formulas for black hole mass, and a method to estimate Doppler factors in blazars.
Findings
Jet power origins differ for BL Lacs and FSRQs.
A dividing line effectively separates blazar types with 84.5% accuracy.
New empirical formula estimates black hole mass from gamma-ray luminosity.
Abstract
Jet origination is one of the most important questions of AGN, yet it stays obscure. In this work, we made use of information of emission lines, spectral energy distributions (SEDs), \textit{Fermi}-LAT -ray emission, construct a blazar sample that contains 667 sources. We notice that jet power originations are different for BL Lacs and for FSRQs. The correlation between jet power and the normalized disk luminosity shows a slope of -1.77 for BL Lacs and a slope of 1.16 for FSRQs. The results seem to suggest that BL Lac jets are powered by extracting blackhole rotation energy, while FSRQ jets are mostly powered by accretion disks. Meanwhile, we find the accretion ratio increase with the normalized -ray luminosity. Base on this, we propose a dividing line, ${\rm log} (L_{\rm BLR}/L_{\rm Edd}) = 0.25 \…
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Taxonomy
TopicsAstrophysics and Cosmic Phenomena · Particle Detector Development and Performance · Computational Physics and Python Applications
