Physics of Particles and Nuclei最新文献

This article explores the application of machine learning techniques to the isochronization of the magnetic field in the MSC 230 isochronous cyclotron. The primary objective is to reduce the computational effort typically required for adjusting the magnet geometry in order to achieve isochronicity. By predicting the necessary modifications to the magnet’s geometry, our approach aims to streamline the iterative process. We compare several machine learning models against traditional methods, demonstrating their potential to reduce the number of iterations needed.

Based on the Holstein Hamiltonian, the charge dynamics in a chain of sites is investigated at finite temperatures. Previously, we compared results obtained for canonical ensemble and for microcanonical description. According to the simulation results, the transition from the polaron mode to the delocalized state occurs in the same range of thermal energy values of a chain of (N) sites with the adjustment: for the microcanonical description the temperature does not correspond to the initially set one, but is determined after long-term calculations from the average kinetic energy of the chain. In this paper, mixed model is considered: first, a polaron is automatically formed in a short chain with Langevin thermostat. Then, the chain is extended by sites with the same temperature. Dynamics of this system in the microcanonical approach, i.e. for a chain in a vacuum, leads to polaron desruption and cooling of the chain. Such effect may be useful in the design of a molecular DNA refrigerator.

This paper presents a detailed examination of difference schemes that establish a one-to-one correspondence between time layers, commonly known as Kahan’s method or reversible difference schemes. These mathematical frameworks play a crucial role in numerical analysis and computational mathematics, enabling accurate modeling of dynamic systems. The paper discusses the applications of these schemes, particularly in dynamic systems with quadratic right-hand sides, which are found in various fields such as physics, engineering, and applied mathematics. These systems often describe complex phenomena, including mechanical vibrations and fluid dynamics. Additionally, the study explores the integration of Kahan’s method with the direct method for solving partial differential equations (PDEs) in mathematical physics. This combination aims to enhance the accuracy and computational efficiency of numerical solutions. By investigating the correlation between these methodologies, this work seeks to advance numerical techniques for addressing complex dynamic systems. The findings indicate that this integration improves the stability and convergence of solutions, highlighting the potential of Kahan’s method and reversible difference schemes in tackling challenges across diverse scientific disciplines.

The dynamics of microtubules is considered in the frameworks of the longitudinal model. By using the approximation of the slowly varying envelopes, we obtain the approximate solution of the model equation in the form of the oscillating kink. The evolution of this solution is compared with the results of the numerical simulation. Several types of the dynamics of the oscillating kinks, which are determined by the parameters of the approximate solution, are identified.

Inclusive jets production in proton-proton collisions at an energy of (sqrt s = 27) GeV in the SPD experiment was studied. The study was performed on the simulation of the (qg to gamma q) process and all QCD processes using the anti-({{k}_{{text{T}}}}) algorithms by Pythia8 generator. The results include determination of kinematic characteristics of hard-scattered partons based on jets properties and with machine learning methods.

Direct computational simulation in naval aerohydromechanics can serve as an available toolkit for engineering research both in the design of new marine equipment and in storm tests, replacing model and full-scale experiments. The latter is due to the increased power of computing systems and the level of detail of mathematical models possible for computer realization. The paper presents an interactive computational environment for modeling the dynamics and control of ship maneuvering. This virtual testbed can be considered both as a navigator’s expert environment and as a simulator for improving skills of effective and safe ship navigation in storm conditions. The paper discusses a reasonable balance between the detail of the applied computational models and computational efficiency. The proposed approach can be implemented in various physical fields, where it is required to develop a virtual testbed to study the behavior of complex technical objects: transport systems, experimental facilities of high-energy physics, etc.

The quasi-elastic collision of an electron with a hydrogen atom at high momentum transfer is considered. Account of the nucleus motion after a kick with a few keV electron leads to unexpected effects in shapes of the double differential cross sections, calculated with the first and second Born approximations (SBA). We discuss the way of numerical calculations of singular SBA integrals with use of the closure approximation.

A modified version of the KANTBP 3.1 program is presented. It implements a stable high-order finite element method for solving a multichannel scattering problem for a system of second-order ordinary differential equations with complex-valued potential matrices. The benchmark calculations of fusion and quasi-elastic cross sections for nuclear reactions ¹⁶O + ⁴⁴Ca and ⁴⁸Ca + ²⁴⁸Cm are provided. A comparison with the outputs of the well-known R-matrix and CCFULL-sc programs is reported.

Optimizing accelerator control is a critical challenge in experimental particle physics, requiring significant manual effort and resource expenditure. Traditional tuning methods are often time-consuming and reliant on expert input, highlighting the need for more efficient approaches. This study aims to create a simulation-based framework integrated with reinforcement learning (RL) to address these challenges. Using Elegant as the simulation backend, we developed a Python wrapper that simplifies the interaction between RL algorithms and accelerator simulations, enabling seamless input management, simulation execution, and output analysis. The proposed RL framework acts as a co-pilot for physicists, offering intelligent suggestions to enhance beamline performance, reduce tuning time, and improve operational efficiency. As a proof of concept, we demonstrate the application of our RL approach to an accelerator control problem and highlight the improvements in efficiency and performance achieved through our methodology. We discuss how the integration of simulation tools with a Python-based RL framework provides a powerful resource for the accelerator physics community, showcasing the potential of machine learning in optimizing complex physical systems.

Supercomputing allows researchers, industry, and stakeholders to use computational models to simulate challenging or impossible conditions to replicate and measure in a laboratory setting. National and regional supercomputing centers provide the computational power to tackle complex problems across various disciplines that require new programming paradigms and runtimes. The paper provides an overview of the Aznavour supercomputer, a national digital infrastructure leveraging existing high-performance computing Big Data infrastructures. Its establishment accelerates scientific discovery and positions Armenia as a critical player in the global tech ecosystem. Aznavour opens up new opportunities for research and development, allowing scientists and engineers to solve problems previously considered impossible and advancing future innovations and technologies. The paper presents the prerequisites for establishing the supercomputing center, tracing its evolution from cluster computing to cloud computing. It also delves into the Aznavour supercomputer’s architecture, detailing its software and hardware components, and highlights the various scientific and engineering communities driving demand for these high-performance computing resources.