The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment. The obtained quantitative performance indicators are still inferior to other approaches, and therefore possible ways to improve the implementation of the method are proposed.
@article{arxiv.2510.20334,
title = {Capability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment},
author = {A. P. Kryukov and A. Yu. Razumov and A. P. Demichev and J. J. Dubenskaya and E. O. Gres and S. P. Polyakov and E. B. Postnikov and P. A. Volchugov and D. P. Zhurov},
journal= {arXiv preprint arXiv:2510.20334},
year = {2025}
}
Comments
9 pages, 4 figures, Proceedings of The 9th International Conference on Deep Learning in Computational Physics, July, 2-4, 2025, Moscow, Russia