In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes ...(IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join.
The Tunka Advanced Instrument for gamma-ray and cosmic ray Astrophysics (TAIGA) is a hybrid observatory for the detection of extensive air showers (EAS), produced by high-energy gamma rays and cosmic ...rays. The complex consists of such facilities as TAIGA-IACT, TAIGA-HiSCORE, and a variety of others. The goal of the study is to introduce a deep learning-based technique for EAS axis reconstruction. A convolutional neural network (CNN) model is proposed, while HiSCORE events, consisting of time-amplitude data, are treated as images by the model. Reasoning behind the CNN model and model efficacy will be discussed, along with preliminary results for EAS axis direction determination. This article will show that the accuracy of the model reaches 1
–2
for the zenith and azimuthal angles, however, the accuracy of the model does not reach the accuracy of conventional methods.
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.
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.
A corrected energy dependence of the depth of the maximum in the wide range of energies 10
15
to 10
18
eV is obtained using data collected at the Tunka-133 facility over 7 years of operation ...(2009–2017) and the TAIGA-HiSCORE facility in the 2019–2020 season. At the highest energies, our results match those of the Pierre Auger observatory. The results are converted to parameter ❬ln
A
❭, which characterizes the mean EAS composition.
Status and First Results of TAIGA Tluczykont, M.; Astapov, I. I.; Awad, A. K. ...
Physics of atomic nuclei,
11/2021, Letnik:
84, Številka:
6
Journal Article
Recenzirano
The Tunka Advanced Instrument for Gamma-ray and cosmic ray Astrophysics (TAIGA) is a hybrid experiment for the measurement of Extensive Air Showers (EAS) with good spectral resolution in the TeV to ...PeV energy range. In this domain, the long-sought Pevatrons can be detected. Currently the TAIGA detector complex combines a two wide angle shower front Cherenkov light sampling timing arrays (HiSCORE and Tunka-133), two 4 m class, 10
aperture Imaging Air Cherenkov Telescopes (IACTs) and 240 m
surface and underground charged particle detector stations. Our goal is to introduce a new hybrid reconstruction technique, combining the good angular and shower core resolution of HiSCORE with the gamma-hadron separation power of imaging air Cherenkov telescopes. This approach allows to maximize the effective area and simultaneously to reach a good gamma-hadron separation at low energies (few teraelectronvolts). At higher energies, muon detectors are planned to enhance gamma-hadron separation. During the commissioning phase of the first and second IACT, several sources were observed. First detections of known sources with the first telescope show the functionality of the TAIGA IACTs. Here, the status of the TAIGA experiment will be presented, along with first results from the current configuration.
The more correct recalculation from the measured Cherenkov light fluxes at distances of 200 (Q200) and 100 (Q100) m from the Extensive Air Shower (EAS) core to the energy of the primary particle has ...been developed using the results of M-C simulation by the CORSIKA code, assuming a light primary composition of cosmic rays. Using the new conversion expressions, a differential energy spectrum was obtained according to the data of the Tunka-133 array for 7 years of operation and the TAIGA-HiSCORE array for 2 years of operation.
The Tunka-Grande experiment Monkhoev, R.D.; Budnev, N.M.; Chiavassa, A. ...
Journal of instrumentation,
06/2017, Letnik:
12, Številka:
6
Journal Article
Recenzirano
The investigation of energy spectrum and mass composition of primary cosmic rays in the energy range 1016–1018 eV and the search for diffuse cosmic gamma rays are of the great interest for ...understanding mechanisms and nature of high-energy particle sources, the problem of great importance in modern astrophysics. Tunka-Grande scintillator array is a part of the experimental complex TAIGA (Tunka Advanced Instrument for Cosmic Ray and Gamma Astronomy) which is located in the Tunka Valley, about 50 km from Lake Baikal. The purpose of this array is the study of diffuse gamma rays and cosmic rays of ultra-high energies by detecting extensive air showers. We describe the design, specifications of the read-out, data acquisition (DAQ) and control systems of the array.
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