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
The aim of processing and analyzing experimental data from physical experiments is to obtain physically significant information about the phenomenon under study. This goal is achieved by multi-stage processing of experimental data, during which noise associated with measurements is suppressed and the dimensionality of the input data is reduced. In this paper, we propose a new method based on the use of neural networks such as autoencoders to extract essential features. The special value of the proposed approach lies in the possibility of its application to the analysis of multimodal data received simultaneously from several installations. We will apply this approach to a multimodal data (MMD) of the experiment TAIGA. Currently, the analysis of the MMD is carried out independently for each installation separately. Therefore, the development of methods for the joint analysis of MMD from TAIGA-type installations is an urgent task in cosmic ray physics and gamma-ray astronomy. Based on Monte Carlo simulation, it is shown that the proposed method allows for effective MMD analysis. It can also be used for MMD analysis at other experimental complexes.
Alexander Kryukov, Julia Dubenskaya, Elena Fedotova +6
Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, 119991 Russia · Research Institute of Applied Physics, Irkutsk State University, Irkutsk, 664003 Russia
We present a neural-network-based data processing pipeline for Baikal-GVD, designed to improve event reconstruction quality and accelerate neutrino candidates selection. The pipeline comprises three stages: fast suppression of extensive air shower events, suppression of noise optical modules activations, and extraction of high confidence neutrino candidates. All three networks employ a transformer architecture that exploits inter-hit correlations through the attention mechanism. Applied sequentially, the pipeline achieves orders-of-magnitude speedup over the standard reconstruction chain. Moreover, noise suppression neural network surpasses the accuracy of algorithmic noise suppression algorithms and provides estimate for time residuals of the signal hits, which is crucial for identification of track-like hits. We address the domain shift between Monte Carlo simulations and experimental data by incorporating a domain adaptation technique, demonstrating improved agreement between the two domains. The resulting framework enables near-real-time event classification, with direct applications to multi-messenger alert systems and diffuse neutrino flux measurements.
A. Matseiko, G. Plotnikov, I. Kharuk
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
Reconstructing the direction of incoming neutrinos in the IceCube Neutrino Observatory is an important problem in astrophysics. The public IceCube--Neutrinos in Deep Ice Kaggle competition provided 140 million simulated events to benchmark reconstruction techniques. To address this challenge from a novel perspective we introduce neutrino fingerprints compact 72×72×3 images in which each pixel represents a single detector, with pulse timing and charge statistics encoded as color channels. This representation transforms sparse, irregular pulse data into dense images suitable for convolutional processing. Our ResNet18 model achieves a mean angular error of 1.10 rad, indicating that convolutional networks trained on fingerprints rival more complex architectures while offering an effective, interpretable baseline for IceCube event reconstruction.
Floriano Tori, Brecht Verbeken, Vincent Ginis
Data Analytics Lab, Vrije Universiteit Brussel, Pleinlaan 5, 1050 Brussels, Belgium · imec-SMIT, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussels, Belgium · School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA