Hunting for the candidates of misclassified sources in LSP BL Lacs using Machine learning
Shi-Ju Kang, Yong-Gang Zheng, Qingwen Wu

TL;DR
This study uses machine learning to identify misclassified blazar sources, revealing potential intrinsic classifications and a transition zone between BL Lacs and FSRQs, which may include changing-look blazars.
Contribution
The paper introduces a machine learning approach to detect misclassified blazars and proposes a transition zone indicating potential changing-look blazars.
Findings
Identified 113 true BL Lacs and 157 false BL Lacs possibly misclassified as FSRQs.
Discovered a transition zone between BL Lacs and FSRQs with implications for blazar classification.
Most LSP changing-look blazars are located in the transition zone.
Abstract
An equivalent width (EW) based classification may cause the erroneous judgement to the flat spectrum radio quasars (FSRQs) and BL Lacerate objects (BL Lac) due to the diluting the line features by dramatic variations in the jet continuum flux. To help address the issue, the present paper explore the possible intrinsic classification on the bias of a random forest supervised machine learning algorithm. In order to do so, we compile a sample of 1680 Fermi blazars that have both gamma-rays and radio-frequencies data available from the 4LAC-DR2 catalog, which includes 1352 training and validation samples and 328 forecast samples. By studying the results for all of the different combinations of 23 characteristic parameters, we found that there are 178 optimal parameters combinations (OPCs) with the highest accuracy ( 98.89\%). Using the combined classification results from the nine…
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Taxonomy
TopicsAstrophysics and Cosmic Phenomena · Computational Physics and Python Applications · Particle physics theoretical and experimental studies
