arXiv subjects

E. B. Postnikov

Publications and source records attributed to E. B. Postnikov.

At least 19 recordsLinked to original sources

arXiv2025

Analysis of the TAIGA-HiSCORE Data Using the Latent Space of Autoencoders

The aim of extensive air shower (EAS) analysis is to reconstruct the physical parameters of the primary particle that initiated the shower. The TAIGA experiment is a hybrid detector system that combines several imaging atmospheric Cherenkov telescopes (IACTs) and an array of non-imaging Cherenkov detectors (TAIGA-HiSCORE) for EAS detection. Because the signals recorded by different detector types differ in physical nature, the direct merging of data is unfeasible, which complicates multimodal analysis. Currently, to analyze data from the IACTs and TAIGA-HiSCORE, a set of auxiliary parameters specific to each detector type is calculated from the recorded signals. These parameters are chosen empirically, so there is no certainty that they retain all important information and are the best suited for the respective problems. We propose to use autoencoders (AE) for the analysis of TAIGA experimental data and replace the conventionally used auxiliary parameters with the parameters of the AE latent space. The advantage of the AE latent space parameters is that they preserve essential physics from experimental data without prior assumptions. This approach also holds potential for enabling seamless integration of heterogeneous IACT and HiSCORE data through a joint latent space. To reconstruct the parameters of the primary particle of the EAS from the latent space of the AE, a separate artificial neural network is used. In this paper, the proposed approach is used to reconstruct the energy of the EAS primary particles based on Monte Carlo simulation data for TAIGA-HiSCORE. The dependence of the energy determination accuracy on the dimensionality of the latent space is analyzed, and these results are also compared with the results obtained by the conventional technique. It is shown that when using the AE latent space, the energy of the primary particle is reconstructed with satisfactory accuracy.

astro-ph.IM↗

arXiv2025

Capability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment

The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment. The obtained quantitative performance indicators are still inferior to other approaches, and therefore possible ways to improve the implementation of the method are proposed.

astro-ph.IM↗

arXiv2025

Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks

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.

astro-ph.IM↗

arXiv2025

Evaluation of EAS directions based on TAIGA HiSCORE data using fully connected neural networks

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.

astro-ph.IM↗

arXiv2025

Gamma/hadron separation in the TAIGA experiment with neural network methods

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 5.5{\sigma} in 21 hours of Crab Nebula observations after processing the experimental data with the neural network method.

astro-ph.IM↗

arXiv2024

Four years of wide-field search for nanosecond optical transients with the TAIGA-HiSCORE Cherenkov array

It has been previously demonstrated [Panov et al. Physics of Atomic Nuclei 84(2021)1037] that the TAIGA-HiSCORE Cherenkov array, originally built for cosmic ray physics and ultrahigh-energy gamma-ray astronomy studies using the extensive air shower method, can be used in conventional optical astronomy for wide-field searches for rare nanosecond optical transients of astrophysical origin. The FOV of the facility is on the scale of 1~ster, and it is capable of detecting very rare transients in the visible light range with fluxes greater than approximately 3000~quanta/m$^2$/10~ns (10~ns is the apparatus integration time) and pulse durations of 10\,ns. Among the potential sources of distant nanosecond optical transients are the evaporation of primary black holes, magnetic reconnection in the accretion disks of black holes, and signals from distant lasers of extraterrestrial civilizations. The paper describes the methods and results of the search for optical transients using the TAIGA-HiSCORE Cherenkov array from 2018 to 2022 (four winter seasons of data collection). No reliable astrophysical candidates for optical transients were found. We set an upper bound on the flux of the searched events as $\sim 1\times10^{-3}$\,events/ster/h.

astro-ph.IM↗

arXiv2021

Search for Astrophysical Nanosecond Optical Transients with TAIGA-HiSCORE Array

A wide-angle Cerenkov array TAIGA-HiSCORE (FOV $\sim$0.6 sr), was originally created as a part of TAIGA installation for high-energy gamma-ray astronomy and cosmic ray physics. Array now consist on nearly 100 optical stations on the area of 1 km$^2$. Due to high accuracy and stability ($\sim$1 ns) of time synchronization of the optical stations the accuracy of EAS arrival direction reconstruction is reached 0.1$^\mathrm{o}$. It was proven that the array can also be used to search for nanosecond events of the optical range. The report discusses the method of searching for optical transients using the HiSCORE array and demonstrates its performance on a real example of detecting signals from an artificial Earth satellite. The search for this short flares in the HiSCORE data of the winter season 2018--2019 is carried out. One candidate for double repeater has been detected, but the estimated probability of random simulation of such a transient by background EAS events is not less than 10%, which does not allow us to say that the detected candidate corresponds to a real astrophysical transient. An upper bound on the frequency of optical spikes with flux density of more than $10^{-4} \mathrm{erg/s/cm}^2$ and a duration of more than 5\,ns is established as $\sim 2 \times 10^{-3}$ events/sr/hour.

astro-ph.IM↗

arXiv2020

Brownian yet non-Gaussian diffusion in heterogeneous media: from superstatistics to homogenization

We discuss the situations under which Brownian yet non-Gaussian (BnG) diffusion can be observed in the model of a particle's motion in a random landscape of diffusion coefficients slowly varying in space. Our conclusion is that such behavior is extremely unlikely in the situations when the particles, introduced into the system at random at $t=0$, are observed from the preparation of the system on. However, it indeed may arise in the case when the diffusion (as described in Ito interpretation) is observed under equilibrated conditions. This paradigmatic situation can be translated into the model of the diffusion coefficient fluctuating in time along a trajectory, i.e. into a kind of the "diffusing diffusivity" model.

cond-mat.stat-mech↗

arXiv2018

Particle identification in ground-based gamma-ray astronomy using convolutional neural networks

Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.

astro-ph.IM↗

arXiv2018

Gamma/Hadron Separation in Imaging Air Cherenkov Telescopes Using Deep Learning Libraries TensorFlow and PyTorch

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.

astro-ph.IM↗

arXiv2018

Remote Access Mössbauer Spectrometry

The authors' design for a Mössbauer spectroscopy unit accessed via the Internet is presented. The spectrometer's configuration, operational peculiarities, key specifications, and remote access procedures are described. More detailed educational, scientific, and technical information can be found at the project's official website http://efmsb.sinp.msu.ru.

physics.ed-ph↗

arXiv2017

Parametric Analysis of Cherenkov Light LDF from EAS for High Energy Gamma Rays and Nuclei: Ways of Practical Application

In this paper we propose a 'knee-like' approximation of the lateral distribution of the Cherenkov light from extensive air showers in the energy range 30-3000 TeV and study a possibility of its practical application in high energy ground-based gamma-ray astronomy experiments (in particular, in TAIGA-HiSCORE). The approximation has a very good accuracy for individual showers and can be easily simplified for practical application in the HiSCORE wide angle timing array in the condition of a limited number of triggered stations.

astro-ph.IM↗

arXiv2017

Hybrid method for identifying mass groups of primary cosmic rays in the joint operation of IACTs and wide angle Cherenkov timing arrays

This work is a methodical study of another option of the hybrid method originally aimed at gamma/hadron separation in the TAIGA experiment. In the present paper this technique was performed to distinguish between different mass groups of cosmic rays in the energy range 200 TeV - 500 TeV. The study was based on simulation data of TAIGA prototype and included analysis of geometrical form of images produced by different nuclei in the IACT simulation as well as shower core parameters reconstructed using timing array simulation. We show that the hybrid method can be sufficiently effective to precisely distinguish between mass groups of cosmic rays.

astro-ph.IM↗

arXiv2017

Primary gamma ray selection in a hybrid timing/imaging Cherenkov array

This work is a methodical study on hybrid reconstruction techniques for hybrid imaging/timing Cherenkov observations. This type of hybrid array is to be realized at the gamma-observatory TAIGA intended for very high energy gamma-ray astronomy (>30 TeV). It aims at combining the cost-effective timing-array technique with imaging telescopes. Hybrid operation of both of these techniques can lead to a relatively cheap way of development of a large area array. The joint approach of gamma event selection was investigated on both types of simulated data: the image parameters from the telescopes, and the shower parameters reconstructed from the timing array. The optimal set of imaging parameters and shower parameters to be combined is revealed. The cosmic ray background suppression factor depending on distance and energy is calculated. The optimal selection technique leads to cosmic ray background suppression of about 2 orders of magnitude on distances up to 450 m for energies greater than 50 TeV.

astro-ph.IM↗

arXiv2017

Background rejection method for tens of TeV gamma-ray astronomy applicable to wide angle timing arrays

A 'knee-like' approximation of Cherenkov light Lateral Distribution Functions, which we developed earlier, now is used for the actual tasks of background rejection methods for high energy (tens and hundreds of TeV) gamma-ray astronomy. In this work we implement this technique to the HiSCORE wide angle timing array consisting of Cherenkov light detectors with spacing of 100 m covering 0.2 km$^2$ presently and up to 5 km$^2$ in future. However, it can be applied to other similar arrays. We also show that the application of a multivariable approach (where 3 parameters of the knee-like approximation are used) allows us to reach a high level of background rejection, but it strongly depends on the number of hit detectors.

astro-ph.IM↗

arXiv2017

Parametric analysis of Cherenkov light LDF from EAS in the range 30-3000 TeV for primary gamma rays and nuclei

A simple 'knee-like' approximation of the Lateral Distribution Function (LDF) of Cherenkov light emitted by EAS (extensive air showers) in the atmosphere is proposed for solving various tasks of data analysis in HiSCORE and other wide angle ground-based experiments designed to detect gamma rays and cosmic rays with the energy above tens of TeV. Simulation-based parametric analysis of individual LDF curves revealed that on the radial distance 20-500 m the 5-parameter 'knee-like' approximation fits individual LDFs as well as the mean LDF with a very good accuracy. In this paper we demonstrate the efficiency and flexibility of the 'knee-like' LDF approximation for various primary particles and shower parameters and the advantages of its application to suppressing proton background and selecting primary gamma rays.

astro-ph.IM↗

arXiv2016

Non-spectral modes and how to find them in the Ornstein-Uhlenbeck process with white μ-stable noise

We consider the Ornstein-Uhlenbeck process with a broad initial probability distribution (Levy distribution), which exhibits so-called non-spectral modes. The relaxation of such modes differs from those determined from the parameters of the corresponding Fokker-Planck equation. The first non-spectral mode is shown to govern the relaxation process and allows for estimation of the initial distribution's Levy index. A method based on continuous wavelet transformation is proposed to extract both (spectral and non-spectral) relaxation rates from a stochastic data sample.

physics.data-an↗

arXiv2016

Isobaric heat capacity, isothermal compressibility and fluctuational properties of 1-bromoalkanes

We present results of the experimental measurements of the isobaric heat capacity for 1-bromohexane, 1-bromoheptane, 1-bromooctane, 1-bromononane, 1-bromodecane, 1-bromoundecane, 1-bromododecane and 1-bromo-tetradecane at normal pressure and the speed of sound and the density for 1-bromotetradecane within the temperature range 298.15--423.15~K. These data on the isobaric heat capacity and the literature-based reference data for the density and the speed of sound were used to calculate the isothermal compressibility and the inverse reduced fluctuations. Based on the comparison of the results for pure n-alkanes and $α,ω$-dibromoalkanes, we discuss the influence of bromine atom on the volume fluctuations.

physics.chem-ph↗