The direction of extensive air showers can be used to determine the source of gamma quanta and plays an important role in estimating the energy of the primary particle. The data from an array of non-imaging Cherenkov detector stations HiSCORE in the TAIGA experiment registering the number of photoelectrons and detection time can be used to estimate the shower direction with high accuracy. In this work, we use artificial neural networks trained on Monte Carlo-simulated TAIGA HiSCORE data for gamma quanta to obtain shower direction estimates. The neural networks are multilayer perceptrons with skip connections using partial data from several HiSCORE stations as inputs; composite estimates are derived from multiple individual estimates by the neural networks. We apply a two-stage algorithm in which the direction estimates obtained in the first stage are used to transform the input data and refine the estimates. The mean error of the final estimates is less than 0.25 degrees. The approach will be used for multimodal analysis of the data from several types of detectors used in the TAIGA experiment.

Modern Imaging Atmospheric Cherenkov Telescopes (IACTs) generate a huge amount of data that must be classified automatically, ideally in real time. Currently, machine learning-based solutions are increasingly being used to solve classification problems. However, these classifiers require proper training data sets to work correctly. The problem with training neural networks on real IACT data is that these data need to be pre-labeled, whereas such labeling is difficult and its results are estimates. In addition, the distribution of incoming events is highly imbalanced. Firstly, there is an imbalance in the types of events, since the number of detected gamma quanta is significantly less than the number of protons. Secondly, the energy distribution of particles of the same type is also imbalanced, since high-energy particles are extremely rare. This imbalance results in poorly trained classifiers that, once trained, do not handle rare events correctly. Using only conventional Monte Carlo event simulation methods to solve this problem is possible, but extremely resource-intensive and time-consuming. To address this issue, we propose to perform data augmentation with artificially generated events of the desired type and energy using conditional generative adversarial networks (cGANs), distinguishing classes by energy values. In the paper, we describe a simple algorithm for generating balanced data sets using cGANs. Thus, the proposed neural network model produces both imbalanced data sets for physical analysis as well as balanced data sets suitable for training other neural networks.

In this work, the ability of rare VHE gamma ray selection with neural network methods is investigated in the case when cosmic radiation flux strongly prevails (ratio up to 10^4 over the gamma radiation flux from a point source). This ratio is valid for the Crab Nebula in the TeV energy range, since the Crab is a well-studied source for calibration and test of various methods and installations in gamma astronomy. The part of TAIGA experiment which includes three Imaging Atmospheric Cherenkov Telescopes observes this gamma-source too. Cherenkov telescopes obtain images of Extensive Air Showers. Hillas parameters can be used to analyse images in standard processing method, or images can be processed with convolutional neural networks. In this work we would like to describe the main steps and results obtained in the gamma/hadron separation task from the Crab Nebula with neural network methods. The results obtained are compared with standard processing method applied in the TAIGA collaboration and using Hillas parameter cuts. It is demonstrated that a signal was received at the level of higher than 5.5σ in 21 h of Crab Nebula observations after processing the experimental data with the neural network method.

The TAIGA-HiSCORE setup is a wide-angle Cherenkov detector array for recording extensive air showers (EASs). The array comprises over 120 stations located in the Tunka Valley near Lake Baikal. One of the main tasks of data analysis in the TAIGA-HiSCORE experiment is to determine the axis direction of the EASs and their core location. These parameters are used to determine the source of gamma rays and play an important role in estimating the energy of the primary particle. The data collected by HiSCORE stations include signal amplitude and arrival time and allow for estimation of the shower direction of arrival. In this work, we use Monte Carlo simulation data for HiSCORE to demonstrate the feasibility of determining the EAS axis directions with artificial neural networks. Our approach employs multilayer perceptrons with skip connections, which take data from subsets of HiSCORE stations as input. Multiple station subsets are selected to derive more accurate composite estimates. Furthermore, we use a two-stage algorithm, where the initial direction estimates in the first stage are refined in the second stage. The final estimates have an average error of less than 0.25 ^∘ . We plan to use this approach as a part of multimodal analysis of data obtained from several types of detectors used in the TAIGA experiment.

Imaging atmospheric cherenkov telescopes (IACTs) of the gamma ray observatory TAIGA detect the extesnive air showers (EASs) originating from the cosmic or gamma rays interactions with the atmosphere. Thereby, telescopes obtain images of the EASs. The ability to segregate gamma rays images from the hadronic cosmic ray background is one of the main features of this type of detectors. However, in actual IACT observations, simultaneous observation of the background and the source of gamma rays is needed. This observation mode (called wobbling) modifies images of events, which affects the quality of selection by neural networks. Thus, in this work, the results of the application of neural networks (NN) for the image classification task on Monte Carlo (MC) images of the TAIGA-IACTs are presented. The wobbling mode is considered together with the image adaptation for the adequate analysis by NNs. Simultaneously, we explore several neural network structures that classify events both directly from images or through Hillas parameters extracted from images. In addition, by employing NNs, MC simulation data are used to evaluate the quality of the segregation of rare gamma events with the account of all necessary image modifications.

The TAIGA experimental complex is a hybrid observatory for high-energy gamma-ray astronomy in the range from 10 TeV to several EeV. The complex consists of such installations as TAIGA- IACT, TAIGA-HiSCORE and a number of others. The TAIGA-HiSCORE facility is a set of wide-angle synchronized stations that detect Cherenkov radiation scattered over a large area. TAIGA-HiSCORE data provides an opportunity to reconstruct shower characteristics, such as shower energy, direction of arrival, and axis coordinates. The main idea of the work is to apply convolutional neural networks to analyze HiSCORE events, considering them as images. The distribution of registration times and amplitudes of events recorded by HiSCORE stations is used as input data. The paper presents the results of using convolutional neural networks to determine the characteristics of air showers. It is shown that even a simple model of convolutional neural network provides the accuracy of recovering EAS parameters comparable to the traditional method. Preliminary results of air shower parameters reconstruction obtained in a real experiment and their comparison with the results of traditional analysis are presented.

The paper is devoted to the modeling and analysis of data detected by the TAIGA-IACT installation in the stereo mode. Five Imaging Atmospheric Cherenkov Telescopes (IACT) with a viewing angle of 9.6° are expected to be included in the installation. Today there are three telescopes spaced far apart (from 320 to 500 m) in the installation. The effective area of the installation is as large as 0.6 km2; therefore, it is possible to conduct statistically significant measurements of weak γ-ray sources in the energy range above 10 TeV over a reasonable observation time (300–400 h). The Monte Carlo procedure for simulating the hadrons and γ-rays detected by the telescopes is described as is the procedure for reconstructing the parameters of extensive air showers, such as the arrival direction of an event, the axis position, the depth of the maximum of shower development (Xmax), and the primary-particle energy. In order to solve the problem of γ-hadron separation, the criteria for selecting γ-rays detected in the stereo mode have been optimized and the effective area of the installation has been calculated.

In recent years, machine learning techniques have seen huge adoption in astronomy applications. In this work, we discuss the generation of realistic synthetic images of gamma-ray events, similar to those captured by imaging atmospheric Cherenkov telescopes (IACTs), using the generative model called a conditional generative adversarial network (cGAN). The significant advantage of the cGAN technique is the much faster generation of new images compared to standard Monte Carlo simulations. However, to use cGAN-generated images in a real IACT experiment, we need to ensure that these images are statistically indistinguishable from those generated by the Monte Carlo method. In this work, we present the results of a study comparing the parameters of cGAN-generated image samples with the parameters of image samples obtained using Monte Carlo simulation. The comparison is made using the so-called Hillas parameters, which constitute a set of geometric features of the event image widely employed in gamma-ray astronomy. Our study demonstrates that the key point lies in the proper preparation of the training set for the neural network. A properly trained cGAN not only excels at generating individual images but also accurately reproduces the Hillas parameters for the entire sample of generated images. As a result, machine learning simulations are a compelling alternative to time-consuming Monte Carlo simulations, offering the speed required to meet the growing demand for synthetic images in IACT experiments.

Imaging atmospheric Cherenkov telescopes are used to record images of extensive area showers caused by high-energy particles colliding with the upper atmosphere. The images are analyzed to determine events’ physical parameters, such as the type and the energy of the primary particles. The distributions of some of the physical parameters can be used as well, for example, to determine the properties of a gamma ray source. The key problem of any experiment is the calibration of experimental data. For this purpose, Monte Carlo simulated data with known values of the physical parameters are used. The main disadvantage of this method is its extremely high requirements for computing resources and the large amount of time spent on modelling. In this paper, we use an alternative approach: Cherenkov telescope images are simulated with conditional variational autoencoders. We compare the characteristics of both the individual images and their Hillas parameter distributions with those of the images generated by the Monte Carlo method.

Generative adversarial networks are a promising tool for image generation in the astronomy domain. Of particular interest are conditional generative adversarial networks (cGANs), which allow you to divide images into several classes according to the value of some property of the image, and then specify the required class when generating new images. In the case of images from Imaging Atmospheric Cherenkov Telescopes (IACTs), an important property is the total brightness of all image pixels (image size), which is in direct correlation with the energy of primary particles. We used a cGAN technique to generate images similar to whose obtained in the TAIGA-IACT experiment. As a training set, we used a set of two-dimensional images generated using the TAIGA Monte Carlo simulation software. We artificiallly divided the training set into 10 classes, sorting images by size and defining the boundaries of the classes so that the same number of images fall into each class. These classes were used while training our network. The paper shows that for each class, the size distribution of the generated images is close to normal with the mean value located approximately in the middle of the corresponding class. We also show that for the generated images, the total image size distribution obtained by summing the distributions over all classes is close to the original distribution of the training set. The results obtained will be useful for more accurate generation of realistic synthetic images similar to the ones taken by IACTs.

High-energy particles hitting the upper atmosphere of the Earth produce extensive air showers that can be detected from the ground level using imaging atmospheric Cherenkov telescopes. The images recorded by Cherenkov telescopes can be analyzed to separate gamma-ray events from the background hadron events. Many of the methods of analysis require simulation of massive amounts of events and the corresponding images by the Monte Carlo method. However, Monte Carlo simulation is computationally expensive. The data simulated by the Monte Carlo method can be augmented by images generated using faster machine learning methods such as generative adversarial networks or conditional variational autoencoders. We use a conditional variational autoencoder to generate images of gamma events from a Cherenkov telescope of the TAIGA experiment. The variational autoencoder is trained on a set of Monte Carlo events with the image size, or the sum of the amplitudes of the pixels, used as the conditional parameter. We used the trained variational autoencoder to generate new images with the same distribution of the conditional parameter as the size distribution of the Monte Carlo-simulated images of gamma events. The generated images are similar to the Monte Carlo images: a classifier neural network trained on gamma and proton events assigns them the average gamma score 0.984, with less than 3 the same time, the sizes of the generated images do not match the conditional parameter used in their generation, with the average error 0.33.

Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical complex allow to observe high energy gamma radiation helping to study many astrophysical objects and processes. TAIGA-IACT enables us to select gamma quanta from the total cosmic radiation flux and recover their primary parameters, such as energy and direction of arrival. The traditional method of processing the resulting images is an image parameterization - so-called the Hillas parameters method. At the present time Machine Learning methods, in particular Deep Learning methods have become actively used for IACT image processing. This paper presents the analysis of simulated Monte Carlo images by several Deep Learning methods for a single telescope (mono-mode) and multiple IACT telescopes (stereo-mode). The estimation of the quality of energy reconstruction was carried out and their energy spectra were analyzed using several types of neural networks. Using the developed methods the obtained results were also compared with the results obtained by traditional methods based on the Hillas parameters.

The imaging Cherenkov telescopes TAIGA-IACT, locate d in the Tunka valley of the republic Buryatia, accumulate a lot of data in a sh ort period of time which must be efficiently and quickly analyzed. One of the method s of such analysis is the machine learning, which has proven its effectiveness in man y technological and scientific fields in recent years. The aim of the work is to study th e possibility of the machine learning application to solve the tasks set for TAIGA-IACT: the identification of the primary particle of cosmic rays and reconstruction their ph ysical parameters. In the work the method of Convolutional Neural Networks (CNN) was a pplied to process and analyze Monte-Carlo events simulated with CORSIKA. Also var ious CNN architectures for the processing were considered. It has been demonstrate d that this method gives good results in the determining the type of primary part icles of Extensive Air Shower (EAS) and the reconstruction of gamma-rays energy. The re sults are significantly improved in the case of stereoscopic observations.