3FGLzoo. Classifying 3FGL Unassociated Fermi-LAT Gamma-ray Sources by Artificial Neural Networks

Abstract

In its first four years of operation, the Fermi Large Area Telescope (LAT) detected 3033 $\gamma$-ray emitting sources. In the Fermi-LAT Third Source Catalogue (3FGL) about 50% of the sources have no clear association with a likely $\gamma$-ray emitter. We use an artificial neural network algorithm aimed at distinguishing BL Lacs from FSRQs to investigate the source subclass of 559 3FGL unassociated sources characterised by $\gamma$-ray properties very similar to those of Active Galactic Nuclei. Based on our method, we can classify 271 objects as BL Lac candidates, 185 as FSRQ candidates, leaving only 103 without a clear classification. we suggest a new zoo for $\gamma$-ray objects, where the percentage of sources of uncertain type drops from 52% to less than 10%. The result of this study opens up new considerations on the population of the $\gamma$-ray sky, and it will facilitate the planning of significant samples for rigorous analyses and multiwavelength observational campaigns.

arXiv comment8 pages, 4 figures, 3 tables, accepted for publication in MNRAS

References in corpus (16)

• The Large Area Telescope on the Fermi Gamma-ray Space Telescope Mission
• The Spectral Energy Distribution of Fermi bright blazars
• The Third Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope
• Spectroscopy of The Largest Ever Gamma-ray Selected BL Lac Sample
• Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques
• Optical spectroscopic observations of $\gamma$-ray blazar candidates VI. Further observations from TNG, WHT, OAN, SOAR and Magellan telescopes
• 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations
• Optical Spectroscopic Observations of Gamma-ray Blazar Candidates. V. TNG, KPNO, and OAN Observations of Blazar Candidates of Uncertain Type in the Northern Hemisphere
• Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks
• Optical spectroscopic observations of gamma-ray blazar candidates III. The 2013/2014 campaign in the Southern Hemisphere
• The Gamma-ray Blazar Quest: new optical spectra, state of art and future perspectives
• Research and characterisation of blazar candidates among the Fermi/LAT 3FGL catalogue using multivariate classifications
• Optical archival spectra of blazar candidates of uncertain type in the 3${}^{rd}$ Fermi Large Area Telescope Catalog
• Looking for blazars in a sample of unidentified high-energy emitting Fermi sources
• Optical Spectra of Candidate Southern Hemisphere International Celestial Reference Frame (ICRF) radio sources
• Optical spectroscopic classification of a selection of Southern Hemisphere Fermi-LAT unclassified blazars

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• The $\gamma$-ray sky seen at X-ray energies II: the Swift hunt of Fermi BL Lac objects among unidentified gamma-ray sources
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• Identifying the 3FHL Catalog. VI. Swift Observations of 3FHL Unassociated Objects with Source Classification via Machine Learning
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• Multiwavelength study of potential blazar candidates among Fermi-LAT unidentified gamma-ray sources