Moscow University Physics Bulletin最新文献

{"title":"Diagnostics of Geoinduced Currents in High Latitude Power Systems Using Machine Learning Methods","authors":"A. V. Vorobev,&nbsp;G. R. Vorobeva","doi":"10.3103/S0027134924702278","DOIUrl":"10.3103/S0027134924702278","url":null,"abstract":"<p>It is known, that geoinduced currents (GICs) take place in spatially distributed current-carrying technical systems (main pipelines, power transmission lines and telegraph lines, railway infrastructure facilities, etc.) due to geomagnetic variations (GMVs), which rate of change in high-latitude regions is often about several hundred nT/min. Flowing through the grounded windings of power transformers of system-forming electrical circuits, extreme GICs are capable of transferring their magnetic systems into saturation mode, which, in turn, can cause a failure of the corresponding electrical systems. However, due to little knowledge of the mechanisms of the emergence and development of GIC, as well as the fragmentation and heterogeneity of the available empirical data, the problem of their predicting and diagnostics today is associated with many uncertainties and remains practically unsolved. The research based on machine learning methods examines approaches to diagnostics of gas and electric power level in the electric network ‘‘Severnyi Transit.’’ In this case, both geomagnetic data recorded by magnetic stations in the subregion (Kola Peninsula, Russia) and natural (visible) indicators of extreme geomagnetic activity are used as input parameters. Using an annual sample of more than 35 000 records as an example, it was shown that the approach to GICs diagnostics, based on multiple linear regression, provides a root mean square error (RMSE) of <span>(sim)</span>0.122 A<span>({}^{2})</span>. The use of an artificial neural network with the ReLU activation function can slightly improve the diagnostic accuracy (RMS <span>(sim)</span> 0.119 A<span>({}^{2})</span>). However, the interpretability and theoretical significance of the model is significantly reduced. The application, in turn, of the Bayesian classifier to the data of optical observations of auroras showed that the posterior probability of the fact that in the north the GIC level at the Vykhodnoy station during auroras will exceed 2 A is 5.78<span>(%)</span>, while the probability of exceeding this value during auroras in the zenith and south are 10.04 and 14.93<span>(%)</span>, respectively. In the absence of auroras, the model indicates that the probability of achieving a GIC of a similar level does not exceed 0.26<span>(%)</span>, and the probability of exceeding 3 A is practically zero.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S807 - S817"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667884","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}

{"title":"Machine Learning Approach in the Prediction of Differential Cross Sections and Structure Functions of Single Pion Electroproduction in the Resonance Region","authors":"A. V. Golda,&nbsp;A. A. Rusova,&nbsp;E. L. Isupov,&nbsp;V. V. Chistyakova","doi":"10.3103/S0027134924702060","DOIUrl":"10.3103/S0027134924702060","url":null,"abstract":"<p>This work explores artificial intelligence methods in the task of predicting differential cross sections in exclusive reactions of positively charged pion production induced by virtual photons. A fully connected neural network devoid of any prior theoretical knowledge about the scatterring process was trained on experimental data from the CLAS detector. We present a comparison of the network’s predictions with experimental data in the form of graphs showing the dependence of differential cross sections on kinematic variables in the excitation energy regions of nucleon resonances, as well as a comparison of the structure functions depending on the values of invariant mass of the final hadron system. Based on this algorithm we can interpolate both the cross-section values and structure function values in different regions of phase space. The neural network approach preserves all correlations of the multidimensional space of kinematic variables, it is model independent and does not consume any a priori knowledge of the process, it is easily extensible to a high dimensional space, which can serve as a good basis for building Monte Carlo event generators or detailed rection analysis.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S608 - S621"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668133","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}

{"title":"Application of Kolmogorov–Arnold Networks in High Energy Physics","authors":"E. E. Abasov,&nbsp;P. V. Volkov,&nbsp;G. A. Vorotnikov,&nbsp;L. V. Dudko,&nbsp;A. D. Zaborenko,&nbsp;E. S. Iudin,&nbsp;A. A. Markina,&nbsp;M. A. Perfilov","doi":"10.3103/S0027134924702035","DOIUrl":"10.3103/S0027134924702035","url":null,"abstract":"<p>Kolmogorov–Arnold Networks represent a recent advancement in machine learning, with the potential to outperform traditional perceptron-based neural networks across various domains as well as provide more interpretability with the use of symbolic formulas and pruning. This study explores the application of KANs to specific tasks in high-energy physics. We evaluate the performance of KANs in distinguishing multijet processes in proton–proton collisions and in reconstructing missing transverse momentum in events involving dark matter.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S585 - S590"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668209","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}

{"title":"Gamma/Hadron Separation in the TAIGA Experiment with Neural Network Methods","authors":"E. O. Gres,&nbsp;A. P. Kryukov,&nbsp;P. A. Volchugov,&nbsp;J. J. Dubenskaya,&nbsp;D. P. Zhurov,&nbsp;S. P. Polyakov,&nbsp;E. B. Postnikov,&nbsp;A. A. Vlaskina","doi":"10.3103/S0027134924702072","DOIUrl":"10.3103/S0027134924702072","url":null,"abstract":"<p>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 <span>(10^{4})</span> 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 <span>(5.5sigma)</span> in 21 h of Crab Nebula observations after processing the experimental data with the neural network method.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S622 - S629"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668137","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}

{"title":"Application of Neural Networks for Path Integrals Computation in Relativistic Quantum Mechanics","authors":"D. V. Salnikov,&nbsp;V. V. Chistiakov,&nbsp;A. V. Vasiliev,&nbsp;A. S. Ivanov","doi":"10.3103/S0027134924702096","DOIUrl":"10.3103/S0027134924702096","url":null,"abstract":"<p>In quantum theory, the expectation value of an observable can be represented as a path integral. In general, it cannot be computed analytically. There are various approximate methods of lattice calculations, for example, the Monte Carlo method. Currently, an approach to solving this problem using neural networks is being developed. In our research, we calculated path integrals in several models of relativistic quantum mechanics using the normalizing flows algorithm. For fast calculations with high accuracy, this algorithm was used in conjunction with the Markov chain generation method.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S639 - S646"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668211","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}

{"title":"Forecasting the State of the Earth’s Magnetosphere Using a Special Algorithm for Working with Multidimensional Time Series","authors":"R. D. Vladimirov,&nbsp;V. R. Shirokiy,&nbsp;O. G. Barinov,&nbsp;S. A. Dolenko,&nbsp;I. N. Myagkova","doi":"10.3103/S0027134924702266","DOIUrl":"10.3103/S0027134924702266","url":null,"abstract":"<p>This study is devoted to the adaptation and application of a special multistage algorithm based on machine learning methods, developed for the analysis of multidimensional time series in solving problems of forecasting certain events and identifying their precursors—phenomena represented by an unknown combination of parameter values describing an object. In addition to forecasting events, the algorithm can be used to forecast the values of continuous quantities. In this study, we compare the results of application of this algorithm in forecasting of three physical quantities characterizing the state of the magnetosphere of the Earth—two geomagnetic indexes (Dst and Kp) and the flux of relativistic electrons (<span>(E&gt;)</span> 2 MeV) in geostationary orbit.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S798 - S806"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668397","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}

{"title":"Probabilistic Programming Methods for Reconstruction of Multichannel Imaging Detector Events: ELVES and TRACKS","authors":"S. A. Sharakin,&nbsp;R. E. Saraev","doi":"10.3103/S0027134924702230","DOIUrl":"10.3103/S0027134924702230","url":null,"abstract":"<p>This paper proposes new methods for analyzing dynamic images registered by multichannel, highly sensitive detectors with low spatial but high temporal resolution. The principal characteristic of the approach is the absence of factorization of different types of information within the data set. For a number of rapidly changing (transient) phenomena in the Earth’s atmosphere, a probabilistic model can be formulated, and the parameters of this model can be reconstructed using probabilistic programming methods (Bayesian inference based on Markov chain Monte Carlo). This paper demonstrates the aforementioned approach on a number of examples, both simulated and actually registered by the detectors of the SINP MSU. In the case of submillisecond ELVES events registered by the orbital Mini-EUSO detector on board the ISS, the probabilistic model includes the coordinates and orientation of the lightning discharge that generated the glow, as well as the height of the ionized layer in which the glow is registered, among its parameters. Bayesian inference, implemented by means of the PyMC library, allows us to calculate posterior distributions for these parameters based on the times of signal peaks in individual detector channels. In addition to studying different types of aurora, the circumpolar system of ground-based multichannel PAIPS detectors also serves as a test-bench for probabilistic reconstruction algorithms. A wide class of track events is used for this purpose—meteors, satellite and aircraft passes, and the movement of stars across the sky. The Bayesian model includes both the parameters of the track event itself and the peculiarities of its registration. These methods can be generalized to stereo events (track registration by two detectors with overlapping fields of view) or applied to the reconstruction of extremely high energy cosmic rays in orbital fluorescence detectors.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S765 - S773"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668395","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}

{"title":"Enhanced Image Clustering with Random-Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature","authors":"V. S. Usatyuk,&nbsp;D. A. Sapozhnikov,&nbsp;S. I. Egorov","doi":"10.3103/S0027134924702102","DOIUrl":"10.3103/S0027134924702102","url":null,"abstract":"<p>This paper addresses the challenge of improving clustering accuracy of image data, particularly focusing on feature representations extracted from convolutional deep neural networks (CNNs). Traditional spectral clustering methods often struggle with high dimension features tensors generated by CNNs like the VGG model. To overcome these limitations, this work proposes a novel approach that enhances spectral clustering by utilizing sparse graph representations (hyperbolic embedding) based on quasi-cyclic low-density parity check (QC-LDPC) and multiedge type (MET) QC-LDPC codes. These graphs are constructed using progressive edge growth (PEG), simulated annealing methods. The paper tackles the specific problem of effectively clustering high-dimensional, sparse image features by modeling their interactions with a random-bond Ising model (RBIM). The optimization process leverages Nishimori temperature estimation to assign weights to graph edges based on image features, leading to more accurate grouping of images into distinct clusters. This approach can be applied to various tasks, including classification. The proposed method not only improves clustering accuracy but also reduces the number of required parameters. It achieves a 17.39<span>(%)</span> improvement in accuracy (90.60<span>(%)</span>) compared to state-of-the-art Erdõs–Rényi graphs (73.21<span>(%)</span>), which lack the hardware-efficient structure of QC-LDPC graphs. By utilizing sparse feature parameters, an efficient MET QC-LDPC multigraph is created that outperforms conventional techniques such as mean-field approximation and Laplacian methods in graph clustering, binary classification. These findings highlight the potential of this approach for a wide range of applications, including image clustering, neural network pruning, data representation, and neuron activation pattern prediction.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S647 - S665"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668134","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}

{"title":"Machine Learning in Gamma Astronomy","authors":"A. P. Kryukov,&nbsp;A. P. Demichev,&nbsp;V. A. Ilyin","doi":"10.3103/S002713492470214X","DOIUrl":"10.3103/S002713492470214X","url":null,"abstract":"<p>The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S690 - S699"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668208","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}

{"title":"Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning","authors":"S. Ali,&nbsp;A. S. Ryzhikov,&nbsp;D. A. Derkach,&nbsp;F. D. Ratnikov,&nbsp;V. O. Bocharnikov","doi":"10.3103/S0027134924702047","DOIUrl":"10.3103/S0027134924702047","url":null,"abstract":"<p>In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors. Leveraging the Wasserstein distance for loss calculation, this innovative approach requires a significantly lower number of events and resources to achieve high precision, minimizing absolute errors effectively. Our work extends the operational lifespan of calorimeters, thereby ensuring the accuracy and reliability of data in the long term, and is particularly beneficial for experiments where data integrity is crucial for scientific discovery.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S591 - S597"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668210","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}