Physics of Particles and Nuclei最新文献

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.

The study of dense baryonic matter formed as a result of relativistic heavy-ion collisions is one of the important researches in High Energy Physics (HEP). The nuclear matter in this phase, called a quark-gluon plasma (QGP), is a mixture of quarks, antiquarks, and gluons when they are freed of their strong attraction for one other under extremely high energy densities. One of the appropriate experiments that can create the most optimal energy conditions for the formation of this matter is Baryonic Matter at Nuclotron (BM@N). A unique experimental setup consisting of various detector subsystems was developed for this experiment. The core of the setup is a hybrid tracker made up of different types of microstrip coordinate detectors to register the trajectories of charged particles produced in primary heavy-ions collisions. It can be conditionally divided into three parts: the beam tracker (SiProf and SiBT), the inner (VSP, FSD and GEM) and outer (CSC) trackers. The aim of the work was to develop the computer model of the aforementioned detectors and prepare the software based on this model for realistic response simulation and reconstruction of spatial coordinates from microstip readout planes. The information given in the work refer to the configuration of the latest experimental run conducted in 2022–2023 (RUN-8) and also for the upcoming run preliminary scheduled for 2025 (RUN-9).

This study addresses foundational challenges in artificial intelligence (AI) arising within the context of multidisciplinary research and explores methodologies for their resolution through the application of machine learning and neural network techniques. A systematic approach is emphasized as a critical framework for accurately formulating problems, particularly in disciplines characterized by low formalization, such as geology and ecology. The research delineates core AI tasks, including retrodiction, forecasting, search optimization, and design synthesis, and discusses solutions grounded in advanced methodologies such as clustering algorithms and regression modeling. A significant focus is placed on the integration of explainable artificial intelligence (XAI) frameworks, which enhance model interpretability, facilitating nuanced insights into complex processes inherent in interdisciplinary investigations. The study also highlights the application of holotypic algorithms, which demonstrate efficacy in resolving classification and object recognition challenges via multidimensional data analysis. This work underscores the transformative role of AI in automating research workflows and optimizing the efficiency of scientific endeavors across interdisciplinary domains.

The precise tuning of the Pythia8 Monte Carlo event generator is essential for accurate modeling of physics phenomena in the Belle II experiment. This work leverages the Professor2 package for multiparameter optimization to fine-tune Pythia8 parameters specifically for event variables. A systematic approach is employed, starting with sensitivity analyses to identify impactful parameters, followed by tuning procedures to align Monte Carlo simulations with experimental data. The tuning process is validated through comprehensive comparisons with both existing Belle tunes and experimental datasets, ensuring improved agreement and robust parameter sets. Results demonstrate significant advancements in modeling off-resonance data, enhancing the reliability of simulated event variables for Belle II analyses.

This paper focuses on advancements in understanding the processes of magnetic field transport in the regime of fully developed stationary magnetohydrodynamic (MHD) chiral turbulence, utilizing the methods of statistical field theory. Within the framework of a model describing homogeneous and isotropic turbulence in the inertial range, an estimation of the transport coefficient α (the so-called α-effect) is provided.

In this paper we consider the model of turbulent diffusion of a passive scalar field in a compressible turbulent flow. The velocity field is modeled by the Kazantsev–Kraichnan “rapid-change” ensemble, while the scalar density field is described by a strongly nonlinear stochastic advection-diffusion equation. As a requirement of renormalizability, the model necessarily involves infinite number of coupling constants. Despite this fact, it is possible to use the renormalization group technique. Renormalization group equations reveal existence of two-dimensional surfaces of fixed points in the infinite-dimensional space of couplings. If some areas on these surfaces involve infrared attractive regions, the problem allows for the large-scale, long-time scaling behaviour. Critical dimensions of the fields and parameters and the spreading law for the particle’s cloud are derived for different scaling regimes.

The paper presents an algorithm for approximate determination of the equilibrium position of a dynamic object making oscillatory motion under the action of several external perturbations. As an example, the rolling of a marine object under the action of waves and wind is considered.