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Journal of Physics G: Nuclear and Particle Physics - latest papers

Latest articles for Journal of Physics G: Nuclear and Particle Physics

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  • Theoretical study of multinucleon transfer reactions with a 208 ...
    Combining with the dinuclear system concept, we developed the traditional master equation approach by incorporating the nucleon transfer probability influenced by the optimal -value. That approach was applied to calculate the production cross sections of the fragments in the Ni+ Pb, Xe+ Pb, and Xe+ Pt reactions and the mass distributions in Ni+ Pb and Xe+ Pb reactions. The calculated results are in favorable agreement with the available experimental data. For the observed asymmetry in the production cross sections of the fragments for Ni+ Pb and Ca+ Pb systems, it is attributed to the constraint of the optimal -value window on the transfer process. Due to the small difference between -value of nucleon transfer and optimal -value, the projectile Ni and Ca tend to pickup neutrons and strip protons. Furthermore, owing to the shell effect, the -values for the Ca+ Pb reaction in the proton pickup and neutron stripping channels are farther from the optimal -value than those for the Ni+ Pb reaction, making it harder for Ca to lose neutrons and gain protons than for Ni.

  • Two-body scattering observables from finite-volume real-time evolution
    We study two-body scattering observables from real-time evolution in a finite periodic box. The system consists of two distinguishable particles on a two-dimensional lattice interacting through pointlike - and -wave interactions. We evolve their wave packets in real time, define detector observables through angular wedges in the relative coordinate, and attach infinite-volume labels obtained from the bound-state pole equation and the low-energy scattering amplitude. We train a convolutional neural network on this data and test its performance on previously unseen finite volume scattering data and find that is able to predict the total magnitude and angular shape of cross sections for previously unseen Hamiltonians.

  • Cross-shell excitations and the impact of the high- j intruder orbital in ...
    The high-spin level structure of the = 51 nucleus Tc has been investigated using large-scale shell-model calculations with different effective interactions. Calculations including neutron excitations across the = 50 shell gap reproduce the main features of the experimental level scheme and identify neutron core excitation as an important mechanism for generating high-spin states. An extended model space including the neutron orbital provides improved agreement with experiment. Wave-function analyses show that configurations dominate over a broad spin range, while = 50 core excitations are largely suppressed. The results reveal the evolution of the competition between excitation and = 50 core breaking with increasing spin.

  • Direct nuclear reaction as a tool for nuclear structure and astrophysics
    The study of nuclear structure has evolved significantly with the exploration of nuclei far from stability, revealing phenomena such as the modification of shell closures, the emergence of halos, and clustering. This review examines direct nuclear reactions as a primary experimental tool for probing these exotic structures and constraining astrophysical reaction rates, with an emphasis on the experimental observables that connect exotic nuclear structure, indirect astrophysical constraints, and modern inverse-kinematics instrumentation. We discuss the theoretical frameworks and experimental applications of elastic and inelastic scattering, transfer reactions, and knockout reactions. Special attention is given to the role of these mechanisms in mapping shell evolution and in characterizing nuclear deformation and -clustering. Furthermore, the review highlights the intersection of nuclear structure and astrophysics, detailing indirect approaches such as the asymptotic normalization coefficient technique and the Trojan horse method for determining key stellar reaction rates at low energies. Finally, state-of-the-art instrumentation is surveyed, including high-resolution spectrometers, active target time projection chambers, and storage rings, which enable high-precision measurements in inverse kinematics.

  • Uncovering hidden systematics in neural network models for high energy physics
    Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flexibility enables unprecedented performance in high-energy physics (HEP) analyses, it also makes them sensitive to small variations in their inputs. Consequently, the propagation and estimation of systematic uncertainties in NN-based models remain an open challenge. There are indications that uncertainties derived in control regions or from nominal variations of input features can underestimate the sensitivity of a trained model, potentially leaving relevant directions in feature space untested. Inspired by adversarial-attack studies in machine learning, we explore how subtle perturbations, bounded by feature-wise experimental resolution scales, can lead to changes in NN outputs while keeping one-dimensional distributions and linear correlations nearly unchanged. Using representative HEP tasks spanning event classification and object identification, we demonstrate that networks can be systematically ‘fooled’ within these prescribed envelopes. We introduce this construction as a stress test of hidden model sensitivity. The resulting performance shift is a worst-case envelope within the assumed, idealized uncertainty model; it is not by itself a calibrated experimental systematic uncertainty or evidence for a bias in nature.