Searching for TeV Candidates in 4LAC High-synchrotron-peaked Frequency BL Lac Objects
K. R. Zhu, S. J. Kang, R. X. Zhou, and Y. G. Zheng

TL;DR
This study uses machine learning to identify new TeV gamma-ray source candidates among high-synchrotron-peaked BL Lac objects from the 4LAC catalog, aiming to guide future high-energy observations.
Contribution
It introduces a machine learning-based method to predict TeV candidates among HBLs, combining multiwavelength data and classification techniques to prioritize targets for upcoming TeV observations.
Findings
24 high-confidence TeV candidates identified
4 candidates likely detectable by LHAASO
24 candidates suitable for CTA observations
Abstract
The next generation of TeV detectors is expected to have a significantly enhanced performance. It is therefore constructive to search for new TeV candidates for observation. This paper focuses on TeV candidates among the high-synchrotron-peaked BL Lacertae objects (HBLs) reported in the fourth catalog of active galactic nuclei detected by the Fermi's Large Area Telescope, i.e., 4LAC. By cross-matching the Fermi data with radio and optical observations, we collected the multiwavelength features of 180 HBLs with known redshift. The data set contains 39 confirmed TeV sources and 141 objects whose TeV detection has not yet been reported (either not yet observed, or observed but not detected). Using two kinds of supervised machine-learning (SML) methods, we searched for new possible TeV candidates (PTCs) among the nondetected objects by assessing the similarity of their multi-wavelength…
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