Malyshev D (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: EDP Sciences
Book Volume: 319
Conference Proceedings Title: EPJ Web of Conferences
Event location: Roma, ITA
DOI: 10.1051/epjconf/202531906009
About one third of Fermi-LAT sources are unassociated. We perform multi-class classification of Fermi-LAT sources using machine learning with the goal of probabilistic classification of the unassociated sources. A particular attention is paid to the fact that the distributions of associated and unassociated sources are different as functions of source parameters. In this work, we address this problem in the framework of dataset shifts in machine learning.
APA:
Malyshev, D. (2025). Classification of Fermi-LAT unassociated sources with machine learning in the presence of dataset shifts. In Antonio Capone, Silvia Celli, Claudio Gasbarra, Aldo Morselli (Eds.), EPJ Web of Conferences. Roma, ITA: EDP Sciences.
MLA:
Malyshev, Dmitry. "Classification of Fermi-LAT unassociated sources with machine learning in the presence of dataset shifts." Proceedings of the 9th Roma International Conference on Astroparticle Physics, RICAP 2024, Roma, ITA Ed. Antonio Capone, Silvia Celli, Claudio Gasbarra, Aldo Morselli, EDP Sciences, 2025.
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