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.

8 pages, 4 figures, 3 tables, accepted for publication in MNRAS

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• Classification of New X-ray Counterparts for Fermi Unassociated Gamma Ray Sources Using the Swift X-Ray Telescope
• Evaluating the classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning
• Identifying the 3FHL catalog: I. Archival Swift Observations and Source Classification
• Artificial Neural Network Classification of 4FGL Sources
• The $\gamma$-ray sky seen at X-ray energies II: the Swift hunt of Fermi BL Lac objects among unidentified gamma-ray sources
• Searching for TeV Candidates in 4LAC High-synchrotron-peaked Frequency BL Lac Objects
• Classification of blazar candidates of unknown type in Fermi 4LAC by unanimous voting from multiple Machine Learning Algorithms
• On the physical association of Fermi-LAT blazars with their low-energy counterparts
• Identifying the 3FHL Catalog. VI. Swift Observations of 3FHL Unassociated Objects with Source Classification via Machine Learning
• Identifying TeV Source Candidates among Fermi-LAT Unclassified Blazars
• A Hybrid method of accurate classification for Blazars Of Uncertain Type in Fermi LAT Catalogs
• Classification of Fermi-LAT blazars with Bayesian neural networks