3FGLzoo: classifying 3FGL unassociated Fermi-LAT γ-ray sources by artificial neural networks

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.