Classification of Fermi blazar cand. from the 4FGL J/ApJ/887/134 ivo://CDS.VizieR/J/ApJ/887/134 doi:10.26093/cds/vizier.18870134 CDS Kang S.-J. Li E. Ou W. Zhu K. Fan J.-H. Wu Q. Yin Y. 2021-07-05T13:06:02Z 2021-06-22T15:25:38Z CDS support team CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France cds-question@unistra.fr active-galactic-nuclei gamma-ray-astronomy bl-lacertae-objects quasars astronomical-models The recently published fourth Fermi Large Area Telescope source catalog (4FGL) reports 5065 gamma-ray sources in terms of direct observational gamma-ray properties. Among the sources, the largest population is the active galactic nuclei (AGNs), which consists of 3137 blazars, 42 radio galaxies, and 28 other AGNs. The blazar sample comprises 694 flat-spectrum radio quasars (FSRQs), 1131 BL Lac-type objects (BL Lacs), and 1312 blazar candidates of an unknown type (BCUs). The classification of blazars is difficult using optical spectroscopy given the limited knowledge with respect to their intrinsic properties, and the limited availability of astronomical observations. To overcome these challenges, machine-learning algorithms are being investigated as alternative approaches. Using the 4FGL catalog, a sample of 3137 Fermi blazars with 23 parameters is systematically selected. Three established supervised machine-learning algorithms (random forests (RFs), support vector machines (SVMs), artificial neural networks (ANNs)) are employed to general predictive models to classify the BCUs. We analyze the results for all of the different combinations of parameters. Interestingly, a previously reported trend the use of more parameters leading to higher accuracy is not found. Considering the least number of parameters used, combinations of eight, 12 or 10 parameters in the SVM, ANN, or RF generated models achieve the highest accuracy (Accuracy ~91.8%, or ~92.9%). Using the combined classification results from the optimal combinations of parameters, 724 BL Lac type candidates and 332 FSRQ type candidates are predicted; however, 256 remain without a clear prediction. 2019ApJ...887..134K https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/887/134 Catalog Research IsServedBy TAP VizieR generic service IsServedBy Conesearch service related-to J/ApJ/700/597 : FERMI LAT detected blazars (Abdo+, 2009) J/ApJ/716/30 : SED of Fermi bright blazars (Abdo+, 2010) J/ApJ/715/429 : First Fermi-LAT AGN catalog (1LAC) (Abdo+, 2010) J/ApJ/743/171 : The 2LAC catalog (Ackermann+, 2011) J/ApJ/753/83 : Associations to 1FGL sources (Ackermann+, 2012) J/MNRAS/424/L64 : AGN/pulsar distinction for 2FGL sources (Mirabal+, 2012) J/MNRAS/428/220 : Gamma-ray AGN type determination (Hassan+, 2013) J/ApJ/782/41 : 231 AGN candidates from the 2FGL catalog (Doert+, 2014) J/ApJ/810/14 : 3rd catalog of LAT-detected AGNs (3LAC) (Ackermann+, 2015) J/MNRAS/451/2750 : Blazar sequence (Xiong+, 2015) J/MNRAS/450/3568 : Non-Fermi blazar sample (Xiong+, 2015) J/other/RAA/16.13 : Sample of Fermi Blazars (Chen+, 2016) J/MNRAS/462/3180 : 3FGL Blazar of Unknown Type classification (Chiaro+, 2016) J/ApJS/226/20 : X-ray, opt. & radio SEDs of Fermi blazars (Fan+, 2016) J/ApJ/820/8 : 3FGL sources statistical classif. (Saz Parkinson+, 2016) J/ApJS/222/24 : VRI LCs of Mrk 501 from 2010 to 2015 (Xiong+, 2016) J/A+A/602/A86 : Blazar cand. among Fermi/LAT 3FGL cat. (Lefaucheur+, 2017) J/MNRAS/470/1291 : Classifying 3FGL with ANN (Salvetti+, 2017) J/ApJS/235/39 : Jet properties of {gamma}-ray-loud 3FGL AGNs (Chen, 2018) J/other/RAA/18.56 : gamma-ray spectrum for Fermi blazars (Kang+, 2018) J/ApJS/247/33 : The Fermi LAT 4th source catalog (4FGL) (Abdollahi+, 2020) https://cds.unistra.fr/vizier-org/licences_vizier.html https://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/887/134 https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/887/134 http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/887/134 https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/887/134 https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/887/134 http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJ/887/134 GET text/xml+votable https://tapvizier.cds.unistra.fr/TAPVizieR/tap Cone search capability for table J/ApJ/887/134/table3 (The classification of Fermi blazar candidates of an unknown type (BCUs)) https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJ/887/134/table3? https://vizier.iucaa.in/viz-bin/conesearch/J/ApJ/887/134/table3? http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJ/887/134/table3? 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Should Not be used for identification. meta.record int Seq [1/1312] Sequential running identifier meta.record int 4FGL 4FGL Source Name meta.id;meta.main char Cntpt Counterpart name meta.id char C4FGL Optical class (always "bcu") (1) meta.code.class char CRF Random Forest classification ("bll": 835 occurrences or "fsrq": 477 occurrences) (2) src.class char PBi-RF [0/1] Probability BCU i belongs to BL Lac using RF stat.fit.goodness float PFi-RF [0/1] Probability BCU i belongs to FSRQ using RF stat.fit.goodness float CSVM Support Vector Machines classification ("bll": 858 occurrences or "fsrq": 454 occurrences) (2) src.class char PBi-SVM [0.016/1] Probability BCU i belongs to BL Lac using SVM stat.fit.goodness float PFi-SVM [0.0005/0.99] Probability BCU i belongs to FSRQ using SVM stat.fit.goodness float CANN Artificial neural network classification ("bll": 868 occurrences or "fsrq": 444 occurrences) (2) src.class char PBi-ANN [0.1/0.9] Probability BCU i belongs to BL Lac using ANN stat.fit.goodness float PFi-ANN [0.1/0.9] Probability BCU i belongs to FSRQ using ANN stat.fit.goodness float Simbad Simbad column added by the CDS meta.ref.url char _RA Right Ascension (J2000) from SIMBAD (not part of the original data) deg pos.eq.ra;meta.main _DE Declination (J2000) from SIMBAD (not part of the original data) deg pos.eq.dec;meta.main
J/ApJ/887/134/table1 The results of the two- test for 1131 BL Lacs and 694 FSRQs recno Record number assigned by the VizieR team. Should Not be used for identification. meta.record int Seq [1/34] Parameter label meta.id int Param Selected parameter meta.note char D-KS [0/0.76] Test statistic D for the two-sample Kolmogorov-Smirnov test stat.fit.param float p1-KS [0/1] p-value (p1) for the two-sample Kolmogorov-Smirnov test stat.fit.goodness double t-tt [-44/27] t-statistic (t) for the Welch two sample t-test stat.fit.param float df-tt [693/1767] Degree of freedom for the t-statistic (df) for the Welch two sample t-test stat.fit.dof int p2-tt p-value(p2) for the Welch two sample t-test stat.fit.goodness char W-W [51341/720456] Continuity correction test statistic (W) for the Wilcoxon rank sum test stat.fit.param int p3-W p-value (p3) for the Wilcoxon rank sum test stat.fit.goodness char Gini [0.01/97.3] The Gini coefficient stat.fit.param float
J/ApJ/887/134/table2 The test accuracy, predict results, and parameters for the optimal combinations par9 [12/23]? Label of parameter (see Table 1) meta.id int nullable recno Record number assigned by the VizieR team. Should Not be used for identification. meta.record int CL Classifier meta.id;stat.fit char Num [8/17] Number of parameters for the optimal combination meta.number int bll [812/923] Number of BL Lacs predicted by a supervised classifier meta.number int fsrq [389/500] Number of FSRQs predicted by a supervised classifier meta.number int Acc [0.918/0.93] Highest accuracies of each classifier stat.fit.goodness float par1 [1/3] Label of parameter (see Table 1) meta.id int par2 [3/11] Label of parameter (see Table 1) meta.id int par3 [5/12] Label of parameter (see Table 1) meta.id int par4 [6/14] Label of parameter (see Table 1) meta.id int par5 [7/16] Label of parameter (see Table 1) meta.id int par6 [8/19] Label of parameter (see Table 1) meta.id int par7 [9/20] Label of parameter (see Table 1) meta.id int par8 [11/23] Label of parameter (see Table 1) meta.id int par10 [14/23]? Label of parameter (see Table 1) meta.id int nullable par11 [15/23]? Label of parameter (see Table 1) meta.id int nullable par12 [17/23]? Label of parameter (see Table 1) meta.id int nullable par13 [18/20]? Label of parameter (see Table 1) meta.id int nullable par14 [19/21]? Label of parameter (see Table 1) meta.id int nullable par15 [20/23]? Label of parameter (see Table 1) meta.id int nullable par16 [22/23]? Label of parameter (see Table 1) meta.id int nullable par17 [23/23]? Label of parameter (see Table 1) meta.id int nullable