Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode
Authors: E. O. Gres, A. P. Kryukov, P. A. Volchigov, A. P. Demichev, J. J. Dubenskaya, D. P. Zhurov, S. P. Polyakov, E. B. Postnikov, +1 more
Organizations: Irkutsk State University, Institute of Applied Physics, Irkutsk, 664003, blv. Gagarina, 20, Russia · Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991 Leninskie gory,1, bld.2, Russia
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.
Figures & tables
Figure 1: Schematic demonstration for multiple neural network classifier for TAIGA-IACTs images: medium image fusion approach (a), early image fusion approach (b) and analysis of autoencoder-derived essential features (c).
Figure 2: Architecture of convolutional analyzers of medium, early fusion approaches and in AE encoder. Conv , MaxPool and BN are abbreviations for convolutional layer, batch normalization and maximum pooling layer respectively.
Figure 3: Validation results for the model of the early fusion approach, using binary crossentropy loss function (a) and focal loss function (b).
Figure 4: Validation classification results for medium fusion approach (top row) and essential feature analysis (bottom row). (a) Validation results with the size-independent classification threshold. (b) Dependence of gamma score on image brightness (size). Red - 50% classification threshold, orange - 90% threshold of remaining gamma.
Classification threshold
Total gamma
Total hadrons
Deep learning method
Selected gamma
Selected protons
Hadron suppression
Size-independent
400
39600
Medium fusion
188
9
4400
Early fusion
206
3
13200
Essential feature analysis
206
3
2475
320
35000
Mono-mode (CNN)
147
41
854
Size-dependent
400
39600
Medium fusion
188
26
1523
Early fusion
203
18
2200
Table 1: Assessment of the various deep learning classifiers in case of stereoscopic and monoscopic observation with IACTs of TAIGA complex
Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991 Russia · Research Institute of Applied Physics, Irkutsk State University, Irkutsk, 664003 Russia
Institute for Nuclear Research of the Russian Academy of Sciences · Moscow Institute of Physics and Technology · Institute for Nuclear Research of the Russian Academy of Sciences, 60th October Anniversary Prospect 7a, Moscow, 117312, Russia +1