Pub Date : 2024-02-01DOI: 10.3103/s0027134924700139
Abstract
Hafnium oxide films HfO({}_{x}) with a thickness of about 40 nm were obtained by electron beam sputtering at different oxygen flow rates in the chamber. The electrophysical properties of the films were studied in air and in a vacuum. It has been shown that the temperature dependences of film conductivity, measured in a vacuum in the temperature range from 20 to 180({}^{circ})C, have an activation character with an activation energy of 0.82(pm) 0.02 eV. It is assumed that in the resulting films, charge transfer is determined by the activation of electrons into the conduction band from the donor level associated with oxygen vacancies. It was found that the conductivity of the films in air changes greatly with varying the oxygen flow, while in a vacuum, the conductivity is practically independent of the oxygen flow. This indicates significant differences in the surface properties of the films obtained at different oxygen flows in the chamber during the deposition process.
{"title":"Conductivity of Hafnium Oxide Films Obtained by Electron-Beam Sputtering","authors":"","doi":"10.3103/s0027134924700139","DOIUrl":"https://doi.org/10.3103/s0027134924700139","url":null,"abstract":"<span> <h3>Abstract</h3> <p>Hafnium oxide films HfO<span> <span>({}_{x})</span> </span> with a thickness of about 40 nm were obtained by electron beam sputtering at different oxygen flow rates in the chamber. The electrophysical properties of the films were studied in air and in a vacuum. It has been shown that the temperature dependences of film conductivity, measured in a vacuum in the temperature range from 20 to 180<span> <span>({}^{circ})</span> </span>C, have an activation character with an activation energy of 0.82<span> <span>(pm)</span> </span> 0.02 eV. It is assumed that in the resulting films, charge transfer is determined by the activation of electrons into the conduction band from the donor level associated with oxygen vacancies. It was found that the conductivity of the films in air changes greatly with varying the oxygen flow, while in a vacuum, the conductivity is practically independent of the oxygen flow. This indicates significant differences in the surface properties of the films obtained at different oxygen flows in the chamber during the deposition process.</p> </span>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140597802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.3103/s002713492470005x
Abstract
The possibilities of engineering energy losses, created in the cascade process of electron-electron scattering during the interaction of multilayer dielectric structures with ionizing radiation, are considered. It is shown that the contribution of surface plasmons associated with layer boundaries to electron energy losses is significant only for nanometer layer thicknesses and increases with increasing electron energy. At the same time, surface states associated with longitudinal optical phonons in ionic crystals significantly change energy losses during electron thermalization and can lead to an increase in the efficiency and rise rate of scintillation in nanostructured systems.
{"title":"Plasmon Energy Losses of Electrons in Multilayer Dielectric Structures","authors":"","doi":"10.3103/s002713492470005x","DOIUrl":"https://doi.org/10.3103/s002713492470005x","url":null,"abstract":"<span> <h3>Abstract</h3> <p>The possibilities of engineering energy losses, created in the cascade process of electron-electron scattering during the interaction of multilayer dielectric structures with ionizing radiation, are considered. It is shown that the contribution of surface plasmons associated with layer boundaries to electron energy losses is significant only for nanometer layer thicknesses and increases with increasing electron energy. At the same time, surface states associated with longitudinal optical phonons in ionic crystals significantly change energy losses during electron thermalization and can lead to an increase in the efficiency and rise rate of scintillation in nanostructured systems.</p> </span>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140597788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.3103/s0027134924700085
Abstract
An analysis of both experimental and theoretical data on the study of preferential sputtering of nickel-based and copper-platinum alloys under ion beam bombardment has been carried out. Contrary to existing models, it has been shown that the main factor determining the process of preferential sputtering is the ratio of the surface binding energies of the components.
{"title":"On Preferential Sputtering of Alloys under Ion Bombardment","authors":"","doi":"10.3103/s0027134924700085","DOIUrl":"https://doi.org/10.3103/s0027134924700085","url":null,"abstract":"<span> <h3>Abstract</h3> <p>An analysis of both experimental and theoretical data on the study of preferential sputtering of nickel-based and copper-platinum alloys under ion beam bombardment has been carried out. Contrary to existing models, it has been shown that the main factor determining the process of preferential sputtering is the ratio of the surface binding energies of the components.</p> </span>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140597698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.3103/s0027134924700073
Abstract
The previously proposed method of molecular dynamics modelling for the sputter deposition of thin films from metal targets has been adapted for the case of dielectric targets and applied to silicon dioxide films. The possibility of the ejection from targets of not only silicon atoms but also clusters with oxygen atoms is taken into account by adding O=Si=O molecules to the flow of deposited atoms. Atomistic film clusters have been obtained at high-energy and low-energy sputter deposition with various percentages of molecules in the flow of deposited atoms. The values of the stress tensor components have been calculated. Compressive stresses are observed at high-energy deposition, while tensile stresses are observed at low-energy deposition. The absolute values of the diagonal components of the stress tensor increase with the increasing proportion of molecules in the flow of deposited atoms.
{"title":"Stresses in Silicon Dioxide Films Deposited from Dielectric Targets: Results of Atomistic Modelling","authors":"","doi":"10.3103/s0027134924700073","DOIUrl":"https://doi.org/10.3103/s0027134924700073","url":null,"abstract":"<span> <h3>Abstract</h3> <p>The previously proposed method of molecular dynamics modelling for the sputter deposition of thin films from metal targets has been adapted for the case of dielectric targets and applied to silicon dioxide films. The possibility of the ejection from targets of not only silicon atoms but also clusters with oxygen atoms is taken into account by adding O=Si=O molecules to the flow of deposited atoms. Atomistic film clusters have been obtained at high-energy and low-energy sputter deposition with various percentages of molecules in the flow of deposited atoms. The values of the stress tensor components have been calculated. Compressive stresses are observed at high-energy deposition, while tensile stresses are observed at low-energy deposition. The absolute values of the diagonal components of the stress tensor increase with the increasing proportion of molecules in the flow of deposited atoms.</p> </span>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140597800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.3103/s0027134923070032
N. Y. Bykov, A. A. Hvatov, T. A. Andreeva, A. Ya. Lukin, M. A. Maslyaev, N. V. Obraztsov, A. V. Surov, A. V. Boukhanovsky
Abstract
The paper presents two methods for discovering differential equations from available data. The first method uses a genetic algorithm with evolutionary optimization, while the second method employs the best subset selection procedure and the Bayesian information criterion. Both methods have been improved to work with noisy and highly sparse data. Diverse techniques for numerical differentiation are proposed, including neural network data approximation and an algorithm for selecting differentiation steps. The proposed approaches are applied to solve physical and engineering problems. As a physical application, the problem of pulsed heating of a viscous liquid by a submerged wire of circular cross section is considered. As an engineering application, the problem of the motion of the arc root along the hollow cylindrical electrode of the alternating current plasma torch is taken. The efficiency of applying approaches for discovering heat transfer models in the form of a partial differential equation and the possibility of the methods to indicate the change in the regimes of the ongoing process are shown. The employment of the model generation approach in the form of a differential equation based on experimental data on the motion of the arc root in the plasma torch made it possible to solve the complex hybrid problem of determining the spatio-temporal distributions of the plasma-forming gas parameters.
{"title":"Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems","authors":"N. Y. Bykov, A. A. Hvatov, T. A. Andreeva, A. Ya. Lukin, M. A. Maslyaev, N. V. Obraztsov, A. V. Surov, A. V. Boukhanovsky","doi":"10.3103/s0027134923070032","DOIUrl":"https://doi.org/10.3103/s0027134923070032","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The paper presents two methods for discovering differential equations from available data. The first method uses a genetic algorithm with evolutionary optimization, while the second method employs the best subset selection procedure and the Bayesian information criterion. Both methods have been improved to work with noisy and highly sparse data. Diverse techniques for numerical differentiation are proposed, including neural network data approximation and an algorithm for selecting differentiation steps. The proposed approaches are applied to solve physical and engineering problems. As a physical application, the problem of pulsed heating of a viscous liquid by a submerged wire of circular cross section is considered. As an engineering application, the problem of the motion of the arc root along the hollow cylindrical electrode of the alternating current plasma torch is taken. The efficiency of applying approaches for discovering heat transfer models in the form of a partial differential equation and the possibility of the methods to indicate the change in the regimes of the ongoing process are shown. The employment of the model generation approach in the form of a differential equation based on experimental data on the motion of the arc root in the plasma torch made it possible to solve the complex hybrid problem of determining the spatio-temporal distributions of the plasma-forming gas parameters.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.3103/s0027134923070056
Yu. Yu. Dubenskaya, A. P. Kryukov, A. P. Demichev, S. P. Polyakov, D. P. Zhurov, E. O. Gres, A. A. Vlaskina
Abstract
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.
{"title":"Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks","authors":"Yu. Yu. Dubenskaya, A. P. Kryukov, A. P. Demichev, S. P. Polyakov, D. P. Zhurov, E. O. Gres, A. A. Vlaskina","doi":"10.3103/s0027134923070056","DOIUrl":"https://doi.org/10.3103/s0027134923070056","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>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.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139500426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.3103/s0027134923070147
I. A. Khabutdinov, M. A. Krinitskiy, R. A. Belikov
Abstract
Cetacean mammal populations, particularly dolphins, have recently experienced significant declines due to various artificial and natural factors. A crucial aspect of studying these populations is determining their numbers and assessing spatial distributions. In our study, we focus on monitoring dolphin populations in the Black Sea using high-resolution photographs taken from helicopters for counting purposes. Currently, expert analysts manually count dolphins in these images, which is a time-consuming process. To address this issue, we propose the use of machine learning (ML) approaches, specifically, anomaly detection using ML models. We examine a dataset collected during accounting marine expeditions of the Shirshov Institute of Oceanology of the Russian Academy of Sciences (IORAS) in the Black Sea from 2018 to 2019. The dataset consists of 3730 high-resolution photographs, with dolphins present in 205 images (5.5(%)). Each dolphin occupies approximately 0.005(%) of an image area (around (49times 49) pixels), making their presence a rare event. Thus, we treat dolphin identification as an anomaly detection task. Our study compares classical and naive anomaly detection methods with reconstruction-based approaches that discriminate anomalies based on the magnitude of reconstruction errors. Within this latter approach, we utilize various artificial neural networks, such as Convolutional Autoencoders (CAE) and U-Net, for image reconstruction. Overall, our research aims to streamline the process of counting and monitoring dolphin populations in high-resolution imagery using advanced ML techniques.
摘要由于各种人为和自然因素，鲸类哺乳动物，特别是海豚的数量最近出现了显著下降。研究这些种群的一个重要方面是确定其数量并评估其空间分布。在我们的研究中，我们主要利用直升机拍摄的高分辨率照片来监测黑海的海豚种群数量。目前，专家分析师需要手动计算这些图像中的海豚数量，这是一个耗时的过程。为了解决这个问题，我们建议使用机器学习（ML）方法，特别是使用 ML 模型进行异常检测。我们研究了俄罗斯科学院希尔绍夫海洋学研究所（IORAS）2018 年至 2019 年在黑海进行会计海洋考察期间收集的数据集。该数据集由 3730 张高分辨率照片组成，其中海豚出现在 205 张图像中（5.5(%)）。每条海豚约占图像面积的0.005（%）（约49（49÷times 49）像素），因此它们的出现非常罕见。因此，我们将海豚识别视为异常检测任务。我们的研究比较了传统的和幼稚的异常检测方法与基于重构的方法，后者根据重构误差的大小来判别异常。在后一种方法中，我们利用了各种人工神经网络，如卷积自动编码器（CAE）和 U-Net 来进行图像重建。总之，我们的研究旨在利用先进的 ML 技术简化高分辨率图像中海豚种群的计数和监测过程。
{"title":"Identifying Cetacean Mammals in High-Resolution Optical Imagery Using Anomaly Detection Approach Employing Machine Learning Models","authors":"I. A. Khabutdinov, M. A. Krinitskiy, R. A. Belikov","doi":"10.3103/s0027134923070147","DOIUrl":"https://doi.org/10.3103/s0027134923070147","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Cetacean mammal populations, particularly dolphins, have recently experienced significant declines due to various artificial and natural factors. A crucial aspect of studying these populations is determining their numbers and assessing spatial distributions. In our study, we focus on monitoring dolphin populations in the Black Sea using high-resolution photographs taken from helicopters for counting purposes. Currently, expert analysts manually count dolphins in these images, which is a time-consuming process. To address this issue, we propose the use of machine learning (ML) approaches, specifically, anomaly detection using ML models. We examine a dataset collected during accounting marine expeditions of the Shirshov Institute of Oceanology of the Russian Academy of Sciences (IORAS) in the Black Sea from 2018 to 2019. The dataset consists of 3730 high-resolution photographs, with dolphins present in 205 images (5.5<span>(%)</span>). Each dolphin occupies approximately 0.005<span>(%)</span> of an image area (around <span>(49times 49)</span> pixels), making their presence a rare event. Thus, we treat dolphin identification as an anomaly detection task. Our study compares classical and naive anomaly detection methods with reconstruction-based approaches that discriminate anomalies based on the magnitude of reconstruction errors. Within this latter approach, we utilize various artificial neural networks, such as Convolutional Autoencoders (CAE) and U-Net, for image reconstruction. Overall, our research aims to streamline the process of counting and monitoring dolphin populations in high-resolution imagery using advanced ML techniques.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139500467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.3103/s0027134923070159
M. A. Krinitskiy, V. A. Golikov, N. N. Anikin, A. I. Suslov, A. V. Gavrikov, N. D. Tilinina
Abstract
Marine radars are vital for safe navigation at sea, detecting vessels and obstacles. Sea clutter, caused by Bragg scattering, is usually filtered out as noise. It becomes detectable in unfiltered radar images, acquired using SeaVision hardware package, when wind speed and wave height exceed certain thresholds. The parameters of wind-induced ocean waves can be determined using these images; however, traditional spectral methods for obtaining wave characteristics face limitations in improving accuracy. Deep learning techniques offer advantages in image processing tasks, being more robust and able to handle noisier data, yet delivering the results without Fourier transformations and not necessarily requiring long series of radar imagery. In our study, we present the method exploiting convolutional neural networks (CNNs) for estimating wave characteristics from shipborne radar data captured using SeaVision package. In particular, we train our CNN to infer significant wave height using estimates provided by the Spotter buoy as ground truth. Our CNN-based method has an advantage over the classical methods due to the low requirements for radar image data since we process just one SeaVision snapshot, whereas classical method requires more than 20 min of radar images.
{"title":"Estimating Significant Wave Height from X-Band Navigation Radar Using Convolutional Neural Networks","authors":"M. A. Krinitskiy, V. A. Golikov, N. N. Anikin, A. I. Suslov, A. V. Gavrikov, N. D. Tilinina","doi":"10.3103/s0027134923070159","DOIUrl":"https://doi.org/10.3103/s0027134923070159","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Marine radars are vital for safe navigation at sea, detecting vessels and obstacles. Sea clutter, caused by Bragg scattering, is usually filtered out as noise. It becomes detectable in unfiltered radar images, acquired using SeaVision hardware package, when wind speed and wave height exceed certain thresholds. The parameters of wind-induced ocean waves can be determined using these images; however, traditional spectral methods for obtaining wave characteristics face limitations in improving accuracy. Deep learning techniques offer advantages in image processing tasks, being more robust and able to handle noisier data, yet delivering the results without Fourier transformations and not necessarily requiring long series of radar imagery. In our study, we present the method exploiting convolutional neural networks (CNNs) for estimating wave characteristics from shipborne radar data captured using SeaVision package. In particular, we train our CNN to infer significant wave height using estimates provided by the Spotter buoy as ground truth. Our CNN-based method has an advantage over the classical methods due to the low requirements for radar image data since we process just one SeaVision snapshot, whereas classical method requires more than 20 min of radar images.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.3103/s0027134923070184
A. P. Kryukov, S. P. Polyakov, A. A. Vlaskina, E. O. Gres, A. P. Demichev, Yu. Yu. Dubenskaya, D. P. Zhurov
Abstract
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.
{"title":"The Use of Conditional Variational Autoencoders for Simulation of EAS Images from IACTs","authors":"A. P. Kryukov, S. P. Polyakov, A. A. Vlaskina, E. O. Gres, A. P. Demichev, Yu. Yu. Dubenskaya, D. P. Zhurov","doi":"10.3103/s0027134923070184","DOIUrl":"https://doi.org/10.3103/s0027134923070184","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>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.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139500418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.3103/s0027134923070123
I. V. Isaev, I. E. Obornev, E. A. Obornev, E. A. Rodionov, M. I. Shimelevich, S. A. Dolenko
Abstract
Exploration geophysics requires solving specific inverse problems — reconstructing the spatial distribution of the medium properties in the thickness of the earth from the geophysical fields measured on its surface. We consider inverse problems of gravimetry, magnetometry, magnetotelluric sounding, and their integration, which means simultaneous use of various geophysical fields to reconstruct the desired distribution. Integration requires the determined parameters for all the methods to be the same. This may be achieved by the spatial statement of the problem, in which the task is to determine the boundaries of geophysical objects. In our previous studies, we considered the parameterization scheme where the inverse problem was to determine the lower boundary of several geological layers. Each layer was characterized by variable values of the depth of the lower boundary along the section, and by fixed values of density, magnetization, and resistivity, both for the layer and over the entire dataset. It was demonstrated that the integration of geophysical methods provides significantly better results than the use of each of the methods separately. The present study considers an extended and more realistic model of data—a parameterization scheme with variable properties of the medium, both along each layer and over the dataset.
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