Milôs Kovačević, Graziano Chiaro,Sara Cutini, Gino Tosti.
This study is the follow on of a first study (B-FlaP) applied to 3FGL catalog (https://arxiv.org/pdf/1607.07822.pdf / https://arxiv.org/abs/1705.09832/arXiv:1808.05881 )
Machine learning is an automatic technique that is revolutionizing the scientific research with innovative applications and the Artificial Neural Networks (ANN) is a powerful machine
learning method widely use in astrophysics. In ten years of operation Fermi detected more than 5000 gamma-ray sources but the number of uncertain sources has exceeded 50% of the detected sources.
ANN algorithms were applied to classify Fermi uncertain sources when strict classifications were not available significantly improving the number of classified objects. The aim of this study was to optimize the precision and effectiveness
of an ANN machine learning method in order to open up new considerations on the population of the gamma-ray sky, and the precision of significant samples selection planning for rigorous analyses and multiwavelength observational campaigns
We will use this new optimized algorithm to classified 4FGL BCUs.