Moscow University Physics Bulletin最新文献

The localized fermionic states arising in an effective field theory model of graphene on the substrate generating a spatially inhomogeneous mass gap were studied. It was shown that in the case of terrace-stepped structure of the substrate inhomogeneity in which the chiral mass has the opposite sign on different terraces, both massless and massive fermionic states are generated. The mass spectrum of such states depends on the size of the mass gap generated by the substrate, as well as the width of the terraces, the number of terraces in the substrate structure, and the width of the transition step.

The tagged neutron method is widely used for position sensitive elemental analysis of various substances and materials. One of the promising applications of this method is the rapid determination of carbon content in soil. The use of portable tagged neutron generators makes it possible to carry out field measurements without preliminary preparation of the samples under study. This article is devoted to the analysis of the possibility of using this method to measure depth profiles of carbon concentration in soil. A setup consisting of a tagged neutron generator ING-27 and 20 BGO-based (gamma)-ray detectors was simulated using GEANT4 and the sensitivity of the method to different layers with various carbon concentration was determined.

The concept of introducing additional target margins has proven effective in photon radiotherapy and, therefore, is a widely accepted method for ensuring the required dose distribution during planning. However, due to the specific interactions of photon radiation with matter in cases of significant tissue heterogeneity, radiotherapy planning necessitates assessing the robustness of the plan or developing a plan resilient to existing dose delivery uncertainties. This study tested the robustness of radiotherapy plans to geometric uncertainties using two irradiation technologies: CRT (conformal radiation therapy) and IMRT (intensity-modulated radiation therapy). A total of 15 patient plans with metallic prostheses were analyzed. The patient’s position relative to the isocenter of the irradiation beams was geometrically shifted to simulate potential patient setup errors. Data on actual displacements obtained during pretreatment visualization—approximately 25 000 treatment fractions for patients with various tumor localizations—were analyzed. According to the results of the study, the probability of not achieving the required dose distribution for the clinical target volume is no more than (0.04pm 0.03%) when using the CRT technique and no more than (7pm 4%) when using IMRT. Thus, the CRT plans demonstrated greater robustness with respect to the target compared to IMRT plans. When IMRT techniques are required for treating patients with prostheses, increased attention must be paid to the patient’s setup and plan robustness verification.

For a singularly perturbed reaction–diffusion equation with periodic coefficients, the structure of the inner transition layer in the case of a balanced reaction with a weak discontinuity is investigated. The existence of periodic solutions with an inner transition layers is proven, their stability is analyzed, and asymptotic approximations of solutions of this type are obtained. It is demonstrated that in the case of reaction balance, the presence of even a weak (asymptotically small) reaction discontinuity can lead to the formation of several periodic contrast structures of finite size, both stable and unstable.

In this paper, retrograde condensation is considered using binary mixtures of hydrocarbons as an example. The feasibility of numerical simulation of this phenomenon using the direct energy minimization is demonstrated. To verify the calculations, similar simulations are performed using the ‘‘classical’’ iterative algorithm for calculating vapor–liquid equilibria in hydrocarbon mixtures. A comparison of the simulation results with the data of physical experiments confirms the prospects of using the direct energy minimization method for phase transition calculations in hydrocarbon mixtures.

In certain pressure and temperature ranges, high-precision thermal equations of state possess multiple roots, with only one being the desired physical solution to the problem. This paper considers methods for setting initial values to calculate the desired root of the equation. A method for verifying the found root has been developed, enabling the exclusion of nonphysical solutions. The proposed approach allows for the construction of a robust algorithm for numerically solving transcendental equations of state for practical applications.

The surface binding energy of atoms in solids is an important parameter for sputtering under ion beam irradiation. In case of multicomponent materials sputtering, such as alloys, the ratio of the alloy components’ surface binding energies determines the preferential sputtering process. In this paper the surface binding energy of atoms in the Ni({}_{x})Pd({}_{y}) alloys with various stoichiometry is calculated using molecular dynamics simulation. The surface binding energy dependence on the alloy components’ concentrations is demonstrated. The surface binding energy temperature dependences and the binding energy for the atoms of the second atomic layer are also calculated.

In the end-cap region of the SPD detector complex, particle identification will be provided by a Focusing Aerogel RICH detector (FARICH). FARICH’s primary function is to separate pions and kaons in final open charmonia states (momenta below 5 GeV/(c)). The optimization of detector parameters, as well as a free-running (triggerless) data acquisition pipeline to be employed in the SPD necessitate a fast and robust method of event reconstruction. In this work, we employ a Convolutional Neural Network (CNN) for particle identification in FARICH. The CNN model achieves a superior separation between pions and kaons compared with traditional approaches. Unlike algorithmic methods, an end-to-end CNN model is able to process a full 2-dimensional detector response and skip the intermediate step of computing particle velocity, solving the particle classification task directly.

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.

In this article, we talk about generating data obtained in the Telescope Array experiment. For this we are using Wasserstein’s generative adversarial networks. Wasserstein’s generative adversarial networks were trained on data obtained using the Monte Carlo method. To improve the quality of the generation, we add the loss function for the generator, which is based on the physics of the process of spreading an extensive air shower. In the future, this network can be used to search for anomalies and for faster data generation, compared with algorithms based on the Monte Carlo method.