Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode
Abstract
In astrophysical complex TAIGA there are four Imaging Atmospheric Cherenkov telescopes (IACTs), located at a distance of 300-500 m from each other, due to which extensive air showers (EAS) generated by cosmic and gamma radiation are recorded by a single telescope or by multiple telescopes simultaneously (stereo-mode). The analysis of EAS images from two or more Cherenkov telescopes together is of particular interest, since for this approach the reconstruction of physical parameters of the primary gamma ray is much more accurate than in mono-observations. However, the gamma-ray event reconstruction strongly depends on the quality of the hadron background suppression of the classifier, therefore, the gamma event selection stage is one of the important ones in the further analysis of IACT data. We propose and study several neural network methods for gamma event selection in joint TAIGA-IACT image analysis. Neural network methods we suggested involves a single-stage analysis of IACT images from two telescopes, while the second method considers the formation of autoencoder-derived essential features that contain the EAS physical parameters, including the type of primary particle.
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BibTeX
@misc{gres2026deep,
title = {{Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode}},
author = {E. O. Gres and A. P. Kryukov and P. A. Volchigov and A. P. Demichev and J. J. Dubenskaya and D. P. Zhurov and S. P. Polyakov and E. B. Postnikov and A. J. Razumov},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16269},
eprint = {2610.16269},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16269},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode
AU - E. O. Gres
AU - A. P. Kryukov
AU - P. A. Volchigov
AU - A. P. Demichev
AU - J. J. Dubenskaya
AU - D. P. Zhurov
AU - S. P. Polyakov
AU - E. B. Postnikov
AU - A. J. Razumov
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.16269
UR - https://arxiv.org/abs/2610.16269
AB - In astrophysical complex TAIGA there are four Imaging Atmospheric Cherenkov telescopes (IACTs), located at a distance of 300-500 m from each other, due to which extensive air showers (EAS) generated by cosmic and gamma radiation are recorded by a single telescope or by multiple telescopes simultaneously (stereo-mode). The analysis of EAS images from two or more Cherenkov telescopes together is of particular interest, since for this approach the reconstruction of physical parameters of the primary gamma ray is much more accurate than in mono-observations. However, the gamma-ray event reconstruction strongly depends on the quality of the hadron background suppression of the classifier, therefore, the gamma event selection stage is one of the important ones in the further analysis of IACT data. We propose and study several neural network methods for gamma event selection in joint TAIGA-IACT image analysis. Neural network methods we suggested involves a single-stage analysis of IACT images from two telescopes, while the second method considers the formation of autoencoder-derived essential features that contain the EAS physical parameters, including the type of primary particle.
ER -