Hunting for the candidates of misclassified sources in LSP BL Lacs using machine learning

Abstract

ABSTRACT An equivalent–width-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, this work explores the possible intrinsic classification on the basis 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 catalogue, 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 parameter combinations (OPCs) with the highest accuracy (≃98.89 per cent). Using the combined classification results from the nine combinations of these OPCs to the 328 forecast samples, we predict that there are 113 true BL Lacs (TBLs) and 157 false BL Lacs (FBLs) that are possible intrinsically FSRQs misclassified as BL Lacs. The FBLs show a clear separation from TBLs and FSRQs in the gamma-ray photon spectral index, Γph, and X-band radio flux, logFR, plot. Phenomenally, existence a BL Lac to FSRQ (B-to-F) transition zone is suggested, where the FBLs are in the stage of transition from BL Lacs to FSRQs. Comparing the LSP changing-look blazars (CLBs) reported in the literatures, the majority of LSP CLBs are located at the B-to-F zone. We argue that the FBLs located at B-to-F transition zone are the most likely candidates of CLBs.