abstract

Two recent measurements of the \(^{12}\mathrm {C}(p,\gamma )^{13}\mathrm {N}\) reaction on natural carbon were performed by detecting \(\beta ^+\) decay of the \(^{13}\mathrm {N}\) residue. It is argued that the measurements at energies above the \(^{13}\mathrm {C}(p,n)^{13}\mathrm {N}\) reaction threshold of 3.24 MeV proton kinetic energy can be interpreted as a consequence of 1.06% admixture of the \(^{13}\mathrm {C}\) isotope in natural carbon, as the cross section for \(^{13}\mathrm {N}\) production raised by 3 orders of magnitude with respect to the measurements of \(^{12}\mathrm {C}(p,\gamma )^{13}\mathrm {N}\) at lower energies.

abstract

Direct detection of bremsstrahlung photons, in principle, offers the most straightforward and most robust method of luminosity determination at the EIC, but requires an extraordinary performance of the photon detector. In this paper, we first discuss the extreme working conditions for such detectors at the EIC and the resulting technology choices. Then, we report on the initial results of Monte Carlo simulations, using Geant4, of the proposed sampling calorimeter, which is made of a copper absorber with embedded quartz fibres read out by silicon photomultipliers. Finally, the tentative requirements for appropriate readout electronics are formulated.

abstract

The AMPT model is used to study elliptic flow \(v_{2}\) in Au\(+\)Au collisions at \(\sqrt {s_{NN}}=200\) GeV, focusing on partonic and hadronic transport processes. By varying the partonic elastic scattering cross section \(\sigma _{p}\) and scaling hadronic cross sections \(\sigma _{H}\), we find that \(v_{2}\) is dominated by partonic interactions with larger \(\sigma _{p}\) accelerating momentum-space anisotropy conversion and suppressing residual spatial anisotropy. Hadronic rescattering minimally impacts \(v_{2}\), only emerging at reduced \(\sigma _{p}\) and higher centrality. The results highlight the critical role of partonic transport in collective flow and the limited influence of hadronic dynamics on final observables.

abstract

Matrix elements of the density matrix of small quantum systems have recently become experimentally accessible within the cold atoms simulations. Numerical estimation of these quantities is known to be computationally challenging. Tracing out a part of the studied system and hence accessing the reduced density matrices offers similar difficulties, but it is more interesting, as the reduced density matrix is the basis of entanglement entropy measures. In this work, we present a method of estimating the individual elements of the reduced density matrices that uses autoregressive neural networks and the path integral quantization approach. We use a hierarchy of neural networks capable of estimating conditional probabilities of consecutive spins to evaluate elements of reduced density matrices directly. Using the Ising chain as an example, we calculate the continuum limit of the ground state’s von Neumann and Rényi bipartite entanglement entropies of an interval built of up to 5 spins. We demonstrate that our architecture is able to estimate all the needed matrix elements with just a single training for a fixed temperature discretization and lattice volume. Our method can be applied to other types of spin chains, possibly with defects, as well as to estimating entanglement entropies of thermal states at non-zero temperature. Hence, it may offer an attractive computational approach as a counterpart to the experimental effort.

abstract

The accurate prediction of proton-induced neutron \((p, n)\) reaction cross sections is critical for applications in nuclear engineering, medical isotope production, and astrophysics. This study introduces a machine learning framework that combines high-predictive accuracy with interpretability and uncertainty quantification. We integrate the Hiking Optimization Algorithm (HOA) with the eXtreme Gradient Boosting (XGBoost) model and employ Monte Carlo (MC) Dropout for uncertainty estimation. The framework is interpreted using SHapley Additive exPlanations (SHAP). First, HOA is utilized to navigate the high-dimensional hyperparameter space, optimizing the XGBoost model for performance. Subsequently, the trained model’s predictions are benchmarked against experimental data from the EXFOR database and theoretical calculations from TALYS 2.0. Our HOA–XGBoost model demonstrates better predictive accuracy over other machine learning models and provides predictions closer to experimental values than TALYS 2.0. The inclusion of MC Dropout provides uncertainty bounds for the model’s predictions. A detailed SHAP analysis reveals the underlying physical drivers of the model’s decisions: the incident proton energy (\(EN\)) is identified as the most influential feature, with its strong interaction with the reaction \(Q\)-value and product proton number (\(Z_2\)) highlighting the model’s ability to learn fundamental concepts such as reaction thresholds and the Coulomb barrier. The product nuclide’s neutron (\(N_2\)) and proton (\(Z_2\)) numbers also show influence related to nuclear stability, while the product mass number (\(A_2\)) has a lesser impact. This work presents a complementary methodology for nuclear data evaluation, paving the way for more reliable predictions and targeted experimental design.

abstract

We investigate electron–positron pair production in single- and double-pulse Sauter-type electric fields, focusing on how temporal separation and field symmetry govern both momentum spectra and entanglement entropy. Using the quantum kinetic approach, we identify a universal three-stage evolution of the entanglement entropy: quasiparticle excitation, a transient oscillatory regime, and residual stabilization. Overlapping pulses produce broadened, irregular spectra with reduced final entropy, whereas well-separated pulses generate regular, high-contrast fringes and enhanced entanglement. This effect is particularly pronounced for antisymmetric configurations. We establish, for the first time, a quantitative link between momentum spectra and entropy: sharper, periodic spectral interference corresponds to stronger correlations between particle–antiparticle modes.

abstract

We study the generalized Lorenz dynamical system describing convective phenomena in magnetized fluid. We employ computer algebra to effectively support the Darboux method of solving differential equations. Darboux polynomials and various types of first integrals are determined.