Speaker
Mr
Francesco Casini
Description
The Fermi-LAT detected more than 7000 γ-ray sources in 14 years of operation. Many of these sources are still unassociated with counterparts in other wavelengths, others are associated to generic classes, but their classification is still unclear. I present a Machine Learning approach to the classification of Fermi-LAT Unidentified Sources and Blazars of Unknown Type using multiwavelength information. I present the Artificial Neural Network methods used to classify the blazars and to find possible multiwavelength counterparts for the Fermi-LAT Unidentified γ-ray Sources. I then conclude by highlighting the uses of the results obtained with this method and the potential future improvements.
