Miloš Kovacevic, Pablo Saz Parkinson, Graziano Chiaro, Giovanni La Mura, Gino Tosti, Sara Cutini.
In the Fermi-LAT Fourth Source Catalogue (3FGL) about 50% of the sources have no clear association with a likely γ-ray emitter. We use machine learning techinque aimed at distinguishing BL Lacs from FSRQs to investigate the source subclass of uncertain (BCU) or unassociated ( UCS) sources characterised by γ-ray properties very similar to those of Active Galactic Nuclei.
This work is a follow up of previous papers : https://arxiv.org/abs/1607.07822 , https://arxiv.org/abs/1705.09832, https://arxiv.org/abs/1808.05881, https://arxiv.org/abs/1602.00385 and will use the 2019 optimization of the original algorithm as described in : Optimizing neural network techniques in classifyingFermi-LAT-ray sources.
The result of this study will suggest a new zoo for 4FGL γ-ray objects, opening up new considerations on the population of the γ-ray sky, and it will facilitating the planning of significant samples for rigorous analyses and multiwavelength observational campaigns.