Evaluating Optical Classification for Fermi Blazar Candidates with a Statistical Method Using Broadband Spectral Indices

Abstract

We aim to test if a blazar candidate of uncertain-type (BCU) in the third Fermi active galactic nuclei catalog (3LAC) can be potentially classified as a BL Lac object or a flat spectrum radio quasar (FSRQ) by performing a statistical analysis of its broadband spectral properties. We find that 34% of the radioselected BCUs (583 BCUs) are BL Lac-like and 20% of them are FSRQ-like, which maybe within 90% level of confidence. Similarly, 77.3% of the X-ray selected BCUs (176 BCUs) are evaluated as BL Lac-like and 6.8% of them may be FSRQlike sources. And 88.7% of the BL Lac-like BCUs that have synchrotron peak frequencies available are high synchrotron peaked BL Lacs in the X-ray selected BCUs. The percentages are accordingly 62% and 7.3% in the sample of 124 optical-selected BCUs. The high ratio of source numbers of the BL Lac-like to the FSRQ-like BCUs in the X-ray and optically selected BCU samples is due to the selection effect. Examining the consistency between our evaluation and spectroscopic identification case by case with a sample of 78 radio-selected BCUs, it is found that the statistical analysis and its resulting classifications agree with the results of the optical follow-up spectroscopic observations. Our observation campaign for high-|ρ s | BCUs , i.e., |ρ s | > 0.8, selected with our method is ongoing.

An Empirical “High-confidence” Candidate Zone for Fermi BL Lacertae Objects

“…We then compared our 120 identified BL Lac candidates with some other recent studies. We found that our results are mostly consistent with previous works presented in Chiaro et al (2016); Lefaucheur & Pita (2017); Yi et al (2017) and Kang et al (2019a) which utilize different statistical (e.g., SML) algorithms (see Table 4 and Table 3). The exceptions are as follows: 2 sources do not find matching sources and 2 sources did not provide a clear classification in Lefaucheur & Pita (2017).…”

“…There are an extra 55 BCUs (obtained easily from the the 3LAC Website version) with a misjudged rate η = 10/414 ≃ 2.415% (see Table 5) in the range (logVI < 1.702 and logF R < 2.258) in the logF R − logVI panel (the upper panel of right column in Figure 1). These sources (57, 22 and 55) have a larger misjudged rate (η > 2.4%); although we did not conclusively evaluate their potential classifications (FSRQs and BL Lacs), it may be helpful for source selection in the spectroscopic observation campaigns in the future to further diagnose their optical classifications (see e.g., Yi et al 2017;Massaro et al 2013 for the some discussions). In addition, if only one parameter is considered, a bigger misjudged error is introduced (see Table 5).…”

“…Estimating the possible classification BL Lac vs FSRQ of BCUs can help to understand which is the most elusive class of blazar hidden in Fermi sample (Massaro et al 2015). Indeed, some potential BL Lac or FSRQ candidates can be identified from the BCUs sample in the 2FGL/3FGL catalogues using different approaches such as supervised machine learning (e.g., support vector machine [SVM] and random forest [RF]; Hassan et al (2013)), neural network (Chiaro et al 2016), artificial neural network (ANN; Salvetti et al 2017), multivariate classification method (Lefaucheur & Pita 2017), and by statisical analysis of the broadband spectral properties (including spectral indices in the gamma-ray, X-ray, optical, and radio bands; Yi et al 2017). In addition, we've identified potential BL Lacs and FS-RQs candidates from the 3LAC Clean sample using 4 different SML algorithms (Mclust Gaussian finite mixture models, Decision trees, RF, and SVM; Kang et al 2019a [Paper I]) and from the 4FGL catalogue using 3 different SML algorithms (ANN, RF, and SVM; Kang et al 2019b).…”

Classification of Fermi BCUs Using Machine Learning

“…Supervised machine learning (SML) is a useful and alternative classification method and it provide reference for classification results. SML had been used by many scholars (Ackermann et al 2012;Chiaro et al 2016;Saz Parkinson et al 2016;Salvetti et al 2017;Lefaucheur & Pita 2017;Yi et al 2017;Kovačević et al 2019Kovačević et al , 2020Kang et al 2019a;Xu et al 2020;Xiao et al 2020;Zhu et al 2021b;Coronado-Blázquez 2022) to classify BCUs from the Fermi-LAT catalogs.…”

The Classification of Blazar Candidates of Uncertain Types

“…The identification of the BCUs is interesting and it can provide more sources for us to investigate the different physics in BL Lacs and FSRQs. The identifications of BCUs were carried out in many works [9,[25][26][27][28][29][30][31][32][33][34] In this work, we apply the support vector machine (SVM) learning method to separate BL Lacs and FSRQs and then use the dividing line to tell BL Lac candidates from FSRQ candidates from the BCUs. The work is arranged as follows: In the 2nd section, a sample, from 4FGL_DR3, used in the work will be described, in 3rd section the distributions of the physical parameters will be given for BL Lacs and FSRQs, and the SVM method will be used to separate BL Lacs and FSRQs, and divide BL Lac candidates and FSRQ candidates, some discussions and conclusions are given in section 4 and section 5.…”