Moscow University Physics Bulletin最新文献_第9页

On the Possibility of Observing Tetraquarks in the (boldsymbol{K^{+}}) Beam 关于在 $$boldsymbol{K^{+}}$ 光束中观测四夸克的可能性

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-03-10 DOI: 10.3103/S002713492306005X

A. S. Gerasimov, A. K. Likhoded, V. A. Petrov, V. D. Samoylenko

引用次数: 0

On Blow-up and Global Existence of Weak Solutions to Cauchy Problem for Some Nonlinear Equation of the Pseudoparabolic Type 论某些伪抛物型非线性方程的柯西问题弱解的膨胀和全局存在性

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-03-10 DOI: 10.3103/S0027134923060097

I. K. Katasheva, M. O. Korpusov, A. A. Panin

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Investigation of the Magnetic, Structural, and Electronic Properties of Pt/[Pt/Co]({}_{mathbf{4}})/Pt Thin Film by Experimental and Theoretical Methods 用实验和理论方法研究 Pt/[Pt/Co] $${}_{mathbf{4}}$$ /Pt 薄膜的磁性、结构和电子特性

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-03-10 DOI: 10.3103/S0027134923060085

Taner Kalayci

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Seismotectonics of the Russian Segment of the Arctic 北极俄罗斯段的地震构造

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-03-10 DOI: 10.3103/S002713492306019X

E. V. Voronina

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On the Influence of ‘‘Red Leak’’ of Light Filters on the Brightness Estimates of Stars of Late Spectral Types Illustrated by the Observations of Rapid Variability of Symbiotic Stars 通过对共生恒星快速变异性的观测说明滤光片 "红漏 "对晚光谱型恒星亮度估计的影响

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-03-10 DOI: 10.3103/S0027134923060139

G. E. Nikishev, N. A. Maslennikova, A. M. Tatarnikov, K. Yu. Parusov, A. A. Belinski

{"title":"On the Influence of ‘‘Red Leak’’ of Light Filters on the Brightness Estimates of Stars of Late Spectral Types Illustrated by the Observations of Rapid Variability of Symbiotic Stars","authors":"G. E. Nikishev, N. A. Maslennikova, A. M. Tatarnikov, K. Yu. Parusov, A. A. Belinski","doi":"10.3103/S0027134923060139","DOIUrl":"10.3103/S0027134923060139","url":null,"abstract":"Results of the simulation of the dependence of the ‘‘red leak’’ magnitude of photometric filters on various factors during star observations are presented: the colour index (V)–(R), luminosity class, magnitude of interstellar reddening, air mass, and precipitable water vapour in the Earth’s atmosphere. The error arising from the neglect of ‘‘red leak’’ in the case of filters used on the 0.6-m telescope of the SAI CMO can be up to (0.6^{m})–(0.8^{m}) for stars of late spectral types. Algorithms for the reduction of observational data for the (U) and (B) filters are presented. The results of observations of the rapid variability of two symbiotic stars CH Cyg and SU Lyn with cold components of very late spectral types are provided. For CH Cyg, rapid variability was detected on both observation dates. Taking into account the ‘‘red leak’’ effect, the amplitude of brightness changes in the (B) band was 0.10({}^{m}) on November 6, 2019 and 0.19({}^{m}) on December 15, 2022, with a characteristic variability time of about 20 min. For SU Lyn, rapid brightness variability in the (B) band on February 25, 2023 was not detected (with an accuracy of up to 0.003({}^{m})).","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"78 6","pages":"854 - 862"},"PeriodicalIF":0.4,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889305","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}

引用次数: 0

On the Choice of Monitoring Procedure of Optical Coating Deposition 关于光学镀膜沉积监控程序的选择

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-03-10 DOI: 10.3103/S0027134923060176

A. V. Tikhonravov, A. A. Lagutina, Iu. S. Lagutin, D. V. Lukyanenko, S. A. Sharapova, A. N. Sharov, A. G. Yagola

引用次数: 0

Novelty Detection Neural Networks for Model-Independent New Physics Search 独立于模型的新物理搜索新颖性检测神经网络

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-01-17 DOI: 10.3103/S0027134923070329

A. D. Zaborenko, P. V. Volkov, L. V. Dudko, M. A. Perfilov

{"title":"Novelty Detection Neural Networks for Model-Independent New Physics Search","authors":"A. D. Zaborenko, P. V. Volkov, L. V. Dudko, M. A. Perfilov","doi":"10.3103/S0027134923070329","DOIUrl":"10.3103/S0027134923070329","url":null,"abstract":"Recent advancements in model-independent approaches in high energy physics have encountered challenges due to the limited effectiveness of unsupervised algorithms when compared to their supervised counterparts. In this paper, we present a novel approach utilizing a one-class deep neural network (DNN) to achieve accuracy levels comparable to supervised learning methods. Our proposed novelty detection algorithm uses a multilayer perceptron to learn and distinguish a specific class from simulated noise signals. By training on a single class, our algorithm constructs a hyperplane similar to one-class support vector machines (SVMs) but with enhanced accuracy and significantly reduced training and inference times. This research contributes to the advancement of model-independent techniques for uncovering New Physics phenomena, showcasing the potential of one-class DNNs as a viable alternative to traditional supervised learning approaches. For the demonstration of the method, the distinguishing of flavour changing neutral currents in top quark interactions from the Standard Model processes has been considered. The obtained results demonstrate the effectiveness of our proposed algorithm, paving the way for improved anomaly detection and exploration of uncharted territories in high energy physics.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"78 1 supplement","pages":"S80 - S84"},"PeriodicalIF":0.4,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139501343","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}

引用次数: 0

Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems 发现偏微分方程的方法：物理和工程问题的应用

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-01-17 DOI: 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

{"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":"10.3103/S0027134923070032","url":null,"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.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"78 1 supplement","pages":"S256 - S265"},"PeriodicalIF":0.4,"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}

引用次数: 0

Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks 利用条件生成对抗网络生成伽马射线事件合成图像，用于大气切伦科夫望远镜成像

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-01-17 DOI: 10.3103/S0027134923070056

Yu. Yu. Dubenskaya, A. P. Kryukov, A. P. Demichev, S. P. Polyakov, D. P. Zhurov, E. O. Gres, A. A. Vlaskina

{"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":"10.3103/S0027134923070056","url":null,"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.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"78 1 supplement","pages":"S64 - S70"},"PeriodicalIF":0.4,"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}

引用次数: 0

Identifying Cetacean Mammals in High-Resolution Optical Imagery Using Anomaly Detection Approach Employing Machine Learning Models 利用机器学习模型的异常检测方法识别高分辨率光学图像中的鲸类哺乳动物

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2024-01-17 DOI: 10.3103/S0027134923070147

I. A. Khabutdinov, M. A. Krinitskiy, R. A. Belikov

{"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":"10.3103/S0027134923070147","url":null,"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.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"78 1 supplement","pages":"S149 - S156"},"PeriodicalIF":0.4,"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}

引用次数: 0