Graziano Chiaro, Milôs Kovačević, Gino Tosti,Sara Cutini, Paolo Giommi, Giovanni LaMura, David J. Thompson
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 )
where the Empirical Cumulative Distribution Functions (ECD) and the Artificial Neural Networks ( ANN) were applied for a classification method for blazar candidates of uncertain type.
This study will consider the preliminary "eight years" list FL8Y and when available the Fourth Fermi Catalog ( 4FGL) .
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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 of the Fermi-LAT gamma telescope 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.