IF 3.1 3区物理与天体物理

Journal of Physics D: Applied Physics Pub Date : 2025-04-07 Epub Date: 2025-02-21 DOI: 10.1088/1361-6463/adb3b4

Huakun Li, Bingyao Tan, Vimal Prabhu Pandiyan, Veluchamy Amutha Barathi, Ramkumar Sabesan, Leopold Schmetterer, Tong Ling

{"title":"Phase-restoring subpixel image registration: enhancing motion detection performance in Fourier-domain optical coherence tomography.","authors":"Huakun Li, Bingyao Tan, Vimal Prabhu Pandiyan, Veluchamy Amutha Barathi, Ramkumar Sabesan, Leopold Schmetterer, Tong Ling","doi":"10.1088/1361-6463/adb3b4","DOIUrl":"10.1088/1361-6463/adb3b4","url":null,"abstract":"Phase-sensitive Fourier-domain optical coherence tomography (FD-OCT) enables in-vivo, label-free imaging of cellular movements with detection sensitivity down to the nanometer scale, and it is widely employed in emerging functional imaging modalities, such as optoretinography (ORG), Doppler OCT, and optical coherence elastography. However, when imaging tissue dynamics in vivo, inter-frame displacement introduces decorrelation noise that compromises motion detection performance, particularly in terms of sensitivity and accuracy. Here, we demonstrate that the displacement-related decorrelation noise in FD-OCT can be accurately corrected by restoring the initial sampling points using our proposed Phase-Restoring Subpixel Image Registration (PRESIR) method. Derived from a general FD-OCT model, the PRESIR method enables translational shifting of complex-valued OCT images over arbitrary displacements with subpixel precision, while accurately restoring phase components. Unlike conventional approaches that shift OCT images either in the spatial domain at the pixel level or in the spatial frequency domain for subpixel correction, our method reconstructs OCT images by correcting axial displacement in the spectral domain (k domain) and lateral displacement in the spatial frequency domain. We validated the PRESIR method through simulations, phantom experiments, and in-vivo ORG in both rodents and human subjects. Our approach significantly reduced decorrelation noise during the imaging of moving samples, achieving phase sensitivity close to the fundamental limit determined by the signal-to-noise ratio.","PeriodicalId":16789,"journal":{"name":"Journal of Physics D: Applied Physics","volume":"58 14","pages":"145102"},"PeriodicalIF":3.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11843479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Operators for Hydrodynamic Modeling of Underground Gas Storages

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702382

D. D. Sirota, K. A. Gushchin, S. A. Khan, S. L. Kostikov, K. A. Butov

{"title":"Neural Operators for Hydrodynamic Modeling of Underground Gas Storages","authors":"D. D. Sirota, K. A. Gushchin, S. A. Khan, S. L. Kostikov, K. A. Butov","doi":"10.3103/S0027134924702382","DOIUrl":"10.3103/S0027134924702382","url":null,"abstract":"Hydrodynamic modeling via numerical simulators of underground gas storages (UGSs) is an integral part of planning and decision-making in various aspects of UGS operation. Although numerical simulators can provide accurate predictions of numerous parameters in UGS reservoirs, in many cases this process can be computationally expensive. In particular, calculation time is one of the major constraints affecting decisions related to optimal well control and distribution of gas injection or withdrawal over the reservoir area. Novel deep learning methods that can provide a faster alternative to traditional numerical reservoir simulators with acceptable loss of accuracy are investigated in this paper. Hydrodynamic processes of gas flow in UGS reservoirs are described by partial differential equations (PDEs). Since PDEs involve approximating solutions in infinite-dimensional function spaces, this distinguishes such problems from traditional ones. Currently, one of the most promising machine learning approaches in scientific computing (scientific machine learning) is the training of neural operators that represent mappings between function spaces. In this paper, a deep learning method for hydrodynamic modeling of UGS is proposed. A modified Fourier neural operator for hydrodynamic modeling of UGS is developed, in which the model parameters in the spectral domain are represented as factorized low-rank tensors. We trained the model on data obtained from a numerical model of UGS with nonuniform discretization grid, more than 100 wells and complex geometry. Our method demonstrates superior performance compared to the original Fourier neural operator (FNO), with an order of magnitude (50 times) fewer parameters. Tensor decomposition not only greatly reduced the number of parameters, but also increased the generalization ability of the model. Developed neural operator simulates a given scenario in a fraction of a second, which is at least (10^{6}) times faster than a traditional numerical solver.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S922 - S934"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668043","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

Generation of Grid Surface Detector Data in the Telescope Array Experiment Using Neural Networks

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702138

R. R. Fitagdinov, I. V. Kharuk

引用次数: 0

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

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702059

Yu. Yu. Dubenskaya, A. P. Kryukov, E. O. Gres, S. P. Polyakov, E. B. Postnikov, P. A. Volchugov, A. A. Vlaskina, D. P. Zhurov

{"title":"Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks","authors":"Yu. Yu. Dubenskaya, A. P. Kryukov, E. O. Gres, S. P. Polyakov, E. B. Postnikov, P. A. Volchugov, A. A. Vlaskina, D. P. Zhurov","doi":"10.3103/S0027134924702059","DOIUrl":"10.3103/S0027134924702059","url":null,"abstract":"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 prelabeled, 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.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S598 - S607"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668136","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

Machine Learning for FARICH Reconstruction at NICA SPD

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702369

F. Shipilov, A. Barnyakov, A. Ivanov, F. Ratnikov

引用次数: 0

Supersymmetric Klein–Gordon and Dirac oscillators

IF 1.3 3区物理与天体物理

Letters in Mathematical Physics Pub Date : 2025-03-22 DOI: 10.1007/s11005-025-01927-y

Alexander D. Popov

{"title":"Supersymmetric Klein–Gordon and Dirac oscillators","authors":"Alexander D. Popov","doi":"10.1007/s11005-025-01927-y","DOIUrl":"10.1007/s11005-025-01927-y","url":null,"abstract":"<div>We have recently shown that the space of initial data (covariant phase space) of the relativistic oscillator in Minkowski space (mathbb {R}^{3,1}) is a homogeneous Kähler–Einstein manifold (Z_6=textrm{AdS}_7/textrm{U}(1) =textrm{U}(3,1)/textrm{U}(3)times textrm{U}(1)). It was also shown that the energy eigenstates of the quantum relativistic oscillator form a direct sum of two weighted Bergman spaces of holomorphic (particles) and antiholomorphic (antiparticles) square-integrable functions on the covariant phase space (Z_6) of the classical oscillator. Here we show that the covariant phase space of the supersymmetric version of the relativistic oscillator (oscillating spinning particle) is the odd tangent bundle of the space (Z_6). Quantizing this model yields a Dirac oscillator equation on the phase space whose solution space is a direct sum of two spinor spaces parametrized by holomorphic and antiholomorphic functions on the odd tangent bundle of (Z_6). After expanding the general solution in Grassmann variables, we obtain components of the spinor field that are holomorphic and antiholomorphic functions from Bergman spaces on (Z_6) with different weight functions. Thus, the supersymmetric model under consideration is exactly solvable, Lorentz covariant and unitary.</div>","PeriodicalId":685,"journal":{"name":"Letters in Mathematical Physics","volume":"115 2","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11005-025-01927-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of Air Pollutants with Thermally Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702205

I. V. Isaev, K. N. Chernov, A. S. Sagitova, V. V. Krivetskiy, S. A. Dolenko

{"title":"Identification of Air Pollutants with Thermally Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models","authors":"I. V. Isaev, K. N. Chernov, A. S. Sagitova, V. V. Krivetskiy, S. A. Dolenko","doi":"10.3103/S0027134924702205","DOIUrl":"10.3103/S0027134924702205","url":null,"abstract":"This study addresses the problem of environmental monitoring of air in cities and industrial areas, which consists in identification of gases and volatile organic compounds using metal oxide (MOX) semiconductor gas sensors. To provide selectivity in the detection of certain gases, the laboratory-made MOX gas sensors are operated in a modulated working temperature mode in combination with signal processing and machine learning approach to establish the response models. Six types of nonlinear operating temperature conditions—the so-called heating dynamics—were applied to twelve sensors with sensing layers of different chemical composition. Nine gases (CO, CH({}_{4}), H({}_{2}), NH({}_{3}), NO, NO({}_{2}), H({}_{2})S, SO({}_{2}), formaldehyde) in six different concentrations each were used as polluting admixtures to dry clean air. Due to the high complexity of the model describing the processes of interaction between gases and sensors, machine learning methods (logistic regression, random forest and gradient boosting) based on the use of physical experiment data were used to process the sensor response. Optimal heating dynamics and optimal machine learning methods for gas identification have been determined.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S731 - S738"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668291","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

Nonlinear Relevance Estimation of Multicollinear Features for Reducing the Input Dimensionality of Optical Spectroscopy Inverse Problem

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702357

N. O. Shchurov, I. V. Isaev, S. A. Burikov, K. A. Laptinskiy, O. E. Sarmanova, T. A. Dolenko, S. A. Dolenko

{"title":"Nonlinear Relevance Estimation of Multicollinear Features for Reducing the Input Dimensionality of Optical Spectroscopy Inverse Problem","authors":"N. O. Shchurov, I. V. Isaev, S. A. Burikov, K. A. Laptinskiy, O. E. Sarmanova, T. A. Dolenko, S. A. Dolenko","doi":"10.3103/S0027134924702357","DOIUrl":"10.3103/S0027134924702357","url":null,"abstract":"In this study we considered an inverse problem of optical spectroscopy. It consists in determining concentrations of the ingredient ions of multicomponent water solutions by their spectra. The problem of describing the spectra of multicomponent solutions is nonlinear and has no adequate mathematical model. Because of this, machine learning methods using experimental data were chosen to solve this problem. At the same time, inverse problems of spectroscopy are characterized by high input dimensionality with a large number of features, more or less relevant. In their turn, some of the relevant features are redundant due to their multicollinearity. This is caused by the fact that the characteristic lines have a width of several spectrum channels. Presence of redundant features leads to a deterioration in the quality of machine learning solution of the problem. Thus, there is a need for a feature selection procedure that takes into account both their relevance and redundancy, as well as their nonlinear relationship with the determined parameters. In this study, we considered a feature selection procedure based on the iterative selection of features with the highest relevance to the target variable and on the elimination of features with a high relationship with each other. In this selection process, we used a trained neural network to analyze weights and determine feature importance in a nonlinear way. We also used the Pearson correlation coefficient to measure how features are related to one another. Finally, we compared the quality of a neural network solution using spectroscopic data of the full set of input features and of its subsets. These subsets were compiled using the selection procedure under consideration. We also used traditional methods for selecting significant input features as baseline methods.","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S898 - S905"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667881","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

Prediction of Defect Structure in MoS({}_{mathbf{2}}) by Given Properties

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702321

H. E. Karlinski, M. V. Lazarev

引用次数: 0

Application of Convolutional Neural Networks for Extensive Air Shower Separation in the SPHERE-3 Experiment

IF 0.4 4区物理与天体物理

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI: 10.3103/S0027134924702126

E. L. Entina, D. A. Podgrudkov, C. G. Azra, E. A. Bonvech, O. V. Cherkesova, D. V. Chernov, V. I. Galkin, V. A. Ivanov, T. A. Kolodkin, N. O. Ovcharenko, T. M. Roganova, M. D. Ziva

引用次数: 0

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