Moscow University Physics Bulletin最新文献

Various models of tetraquark generation in the reaction (K^{+}prightarrow T(us;bar{s}bar{s})X) are considered. The predictions for corresponding inclusive spectra were evaluated at the energies 32 and 250 GeV.

We briefly present the results of the investigation of the Cauchy problem for a nonlinear pseudoparabolic equation that is a mathematical generalisation of a certain model in semiconductor theory. The potential theory for the linear part of the equation is elaborated, which demands quite laborious technique, which can be applied for other equations. The properties of the fundamental solution of this linear part are also of interest because its 1st time derivative possesses a singularity. This is not usual for equations of the considered type. Moreover, sufficient conditions for global-in-time solvability are obtained in the paper, as well as sufficient conditions for its finite-time blow-up.

In this study, the magnetic, structural, and electronic properties of Pt/[Pt/Co]({}_{4})/Pt thin film was investigated both experimentally and theoretically. The effects of crystal orientation on magnetic behavior in the primitive cell were investigated via the first-principles methods. Band structures, total and partial density of states was calculated as the electronic properties. Magneto-optical Kerr effect and ferromagnetic resonance techniques were carried out to determine magnetic properties. The magnetic behavior of Pt/[Pt/Co]({}_{4})/Pt in microscopic framework is revealed by the spin asymmetry in the density of states around the Fermi level. The perpendicular magnetic anisotropy is found to be more favorable for the Pt/[Pt/Co]({}_{4})/Pt with (111) orientation. It was seen that the crystal orientation of Pt/[Pt/Co]({}_{4})/Pt has a critical role on the magnetic properties according to the band magnetism calculations.

The study examines the foci of the strongest earthquakes in the Russian segment of the Arctic, which have occurred over the entire period of observations, starting from 1976 to the present. The stress and strain fields have been studied through the analysis of the seismic moment tensor of the registered earthquakes. This analysis is conducted for the first time. Spatial distributions of the Lode–Nadai coefficient and the rate of seismotectonic strain have been obtained.

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})).

Theoretical results are formulated to assess the strength of the effect of self-compensation of errors in layer thicknesses of multilayer optical coatings. They are applicable to any method of optical monitoring of the deposition process. It is shown that considering a possible presence of a strong error self-compensation effect is of great importance for choosing a monitoring method. A comparative analysis of the results obtained to date to assess the strength of the error self-compensation effect for various types of coatings has been carried out. Moreover, in this work, a number of results were obtained directly for the first time. The results obtained can be used to select the optimal method for monitoring the deposition process depending on the type of coating.

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.

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.

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.

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.