Evaluating the Optical Classification of Fermi BCUs Using Machine Learning

Abstract

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).

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).…”

“…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). In addition, only 3 sources are classified as FSRQs in M clust Gaussian Mixture Modelling (M 8 ), and two are classified as FRSQs using support vector machine (SVM 8 ) using 8 parameters in Kang et al 2019a; 1 source is classified as an FSRQ in Chiaro et al 2016 (Chi16), whereas two sources are classified as FRSQs in Yi et al 2017 (Y17). The results, provided in Table 4, indicate the highest mismatch rate (e.g., rate = 3/120% ∼2.5%) is less than 3%.…”

“…The BL Lac candidates using the "ai < X candidate zone" are listed in Column 7. Columns 8-11 (M8, DT8, RF8, and SVM8) indicate the BL Lac -type ("bll") candidates identified by 4 different supervised machine learning (SML) algorithms (Mclust Gaussian finite mixture models (M8), Decision trees (DT8), Random forests (RF8) and support vector machines (SVM8)) with 8 parameters in Kang et al 2019a. Column 12 (LP17) lists the classifications ("bll" for BL Lac, "unc" for uncertain and "-" for a mismatched source by cross comparison) in Lefaucheur 36 118 109 110 108 105 4 bll 120 117 120 120 118 119 116 118 24 41 2 11 10 12 15 63 74 fsrq 0 3 0 0 2 1 0 2 0 0 0 0 0 0 0 1 unc 2 43 52 Note-Column 1 shows the classifications (− represents the number of mismatch by cross comparison, "bll" ,"fsrq" and "unc" indicate BL Lac, FSRQ and uncertain type respectively).…”

Evaluating the Classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning

“…In addition, we also should note that all of the default settings (e.g., the probabilities: p > 0.5 in each classifier to consider a correct classification, see Table 3) for each of the three classification functions (e.g., randomF orest(), svm() and nnet() function) are used in Section 4. For each different classification method, choosing the calculation model and setting each parameter in the fitting function can also affect the predictive models, accuracy, and results (e.g., see the discussions in Kang et al 2019). The selections of the appropriate parameter settings need further investigation; this is beyond the scope of the current work.…”

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

“…Duev et al () trained a CNN to discover fast‐moving candidates from ZTF observations in order to more reliably identify potentially hazardous near‐Earth objects. Active galactic nuclei and quasars . A common theme in this field is the need for classification and detection methods, including assigning morphological types to radio‐detected active galactic nuclei with a CNN (Ma et al, ), identifying blazar candidates in the Fermi‐LAT (3LAC) Clean Sample (Kang et al, ), detecting rare high‐redshift, extremely luminous quasars (Schindler et al, ), and discriminating populations of broad absorption line quasars (BALQs) from non‐BALQs in SDSS data releases (Yong et al, ). Cosmological simulations . ML is providing new methods for examining the outputs of cosmological simulations, leading to new insights about the connections between physical properties of galaxies, dark matter halos and the cosmic environment.…”