Capability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment
This is an incremental improvement for astrophysics experiments like TAIGA, aiming to enhance gamma event detection.
The paper tackled the problem of detecting rare gamma quanta against charged particle backgrounds in cosmic flux data using a deep learning and normalizing flows method for anomaly detection, showing potential but with performance indicators still inferior to other approaches.
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.