Classification of Fermi blazar cand. from the 4FGL

DOI

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.

Cone search capability for table J/ApJ/887/134/table3 (The classification of Fermi blazar candidates of an unknown type (BCUs))

Identifier
DOI http://doi.org/10.26093/cds/vizier.18870134
Source https://dc.g-vo.org/rr/q/lp/custom/CDS.VizieR/J/ApJ/887/134
Related Identifier https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/887/134
Related Identifier http://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/887/134
Metadata Access http://dc.g-vo.org/rr/q/pmh/pubreg.xml?verb=GetRecord&metadataPrefix=oai_b2find&identifier=ivo://CDS.VizieR/J/ApJ/887/134
Provenance
Creator Kang S.-J.; Li E.; Ou W.; Zhu K.; Fan J.-H.; Wu Q.; Yin Y.
Publisher CDS
Publication Year 2021
Rights https://cds.unistra.fr/vizier-org/licences_vizier.html
OpenAccess true
Contact CDS support team <cds-question(at)unistra.fr>
Representation
Resource Type Dataset; AstroObjects
Discipline Astrophysics and Astronomy; Galactic and extragalactic Astronomy; High Energy Astrophysics; Natural Sciences; Observational Astronomy; Physics; Stellar Astronomy