Evaluating the Optical Classification of Fermi BCUs Using Machine Learning

In the third catalog of active galactic nuclei detected by the Fermi-LAT (3LAC) Clean Sample, there are 402 blazar candidates of uncertain type (BCUs). Due to the limitations of astronomical observation or intrinsic properties, it is difficult to classify blazars using optical spectroscopy. The potential classification of BCUs using machine-learning algorithms is essential. Based on the 3LAC Clean Sample, we collect 1420 Fermi blazars with eight parameters of γ-ray photon spectral index; radio flux; flux density; curve significance; the integral photon flux in 100-300 MeV, 0.3-1 GeV, and 10-100 GeV; and variability index. Here we apply four different supervised machine-learning (SML) algorithms (decision trees, random forests, support vector machines, and Mclust Gaussian finite mixture models) to evaluate the classification of BCUs based on the direct observational properties. All four methods can perform exceedingly well with more accuracy and can effectively forecast the classification of Fermi BCUs. The evaluating results show that the results of these methods (SML) are valid and robust, where about one-fourth of sources are flat-spectrum radio quasars (FSRQs) and three-fourths are BL Lacertae (BL Lacs) in 400 BCUs, which are consistent with some other recent results. Although a number of factors influence the accuracy of SML, the results are stable at a fixed ratio 1:3 between FSRQs and BL Lacs, which suggests that the SML can provide an effective method to evaluate the potential classification of BCUs. Among the four methods, Mclust Gaussian Mixture Modeling has the highest accuracy for our training sample (4/5, seed = 123).