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

## TL;DR

This paper employs artificial neural networks to classify unassociated Fermi-LAT gamma-ray sources, significantly reducing the uncertain source population and aiding future astrophysical research and observational planning.

## Contribution

It introduces a neural network-based method to classify unassociated gamma-ray sources, decreasing uncertain classifications from 52% to less than 10%.

## Key findings

- Classified 271 sources as BL Lac candidates.
- Classified 185 sources as FSRQ candidates.
- Reduced uncertain source percentage from 52% to under 10%.

## 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.