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Nuclear Theory·nucl-th

Monday·November 15, 2021

8 papers—4 primary·4 cross-listed

All8Primary4Cross-lists4
  1. 01

    Particle configurations in the system

    Igor Filikhin🇺🇸 · Yury B. Kuzmichev🇷🇺 · Branislav Vlahovic🇺🇸

    Three-body model for the kaonic cluster is considered based on the configuration space Faddeev equations. Within a single-channel approach, the difference between masses of nucleons and kaons and the charge independence breaking of nucleon-nucleon interaction are taken into consideration. We definite the particle configurations in the system according to the particle masses and pair potentials. There are two sets of the particle configurations, , and , , charged and neutral. The three-body calculations are performed by applying and phenomenological isospin-dependent potentials. The mass and energy spectra related to the particle configurations are presented. We evaluate the mass and energy uncertainties for the model. An analogy to model for the H and He nuclei is proposed.

    nucl-thMath.Model.Geom.(2021)·1 citation
  2. 02

    Smoothing of one- and two-dimensional discontinuities in potential energy surfaces

    N.-W. T. Lau (1 and 2) · R. N. Bernard (1) · C. Simenel (1 and 2) ((1) Department of Fundamental and Theoretical Physics, Research School of Physics, Australian National University, Canberra, Australia (2) Department of Nuclear Physics and Accelerator Applications, Research School of Physics, Australian National University, Canberra, Australia)

    Background: The generation of potential energy surfaces is a critical step in theoretical models aiming to understand and predict nuclear fission. Discontinuities frequently arise in these surfaces in unconstrained collective coordinates, leading to missing or incorrect results. Purpose: This work aims to produce efficient and physically-motivated computational algorithms to refine potential energy surfaces by removing discontinuities. Method: Procedures based on tree-search algorithms are developed which are capable of smoothing discontinuities in one and two-dimensional potential energy surfaces while minimising their overall energy. Results: Each of the new methods is applied to smooth candidate discontinuities in , and . The effectiveness of each case is analysed both qualitatively and quantitatively. The one-dimensional method is also compared to the adiabatic and linear interpolation approaches which are commonly used to remove discontinuities. Conclusions: The smoothing methods presented in this work are resource-efficient and successful for one- and two-dimensional discontinuities; they will improve the fidelity of potential energy surfaces as well as their subsequent uses in beyond mean-field applications. Complex discontinuities occurring in higher dimensions may require alternative approaches which better utilise prior knowledge of the potential energy surface to narrow their searches.

    nucl-thPRC(2022)·14 citations
  3. 03

    Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network

    X. Zhang · Y. Huang · W. Lin · X. Liu · H. Zheng · R. Wada · A. Bonasera · Z. Chen · L. Chen · J. Han · R. Han · M. Huang and 9 other authors

    A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed using the constrained molecular dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several ten to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than 3 become crucial. To make the CNN applicable in the task of the impact parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact parameter-sensitive observables, that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds, and shows significantly better in terms of predicting the impact parameters and recognizing the central collision events, compared to that of the conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact parameter determination using the deep CNN method, compared to using the the conventional methods. This result reveals the importance to select a reliable impact parameter determination method in the experimental deduction of the nuclear stopping power as well as other observables.

    nucl-thPRC(2022)·23 citations
  4. 04

    SimpleTensor -- a user-friendly Mathematica package for elementary tensor and differential-geometric calculations

    D. O. Rybalka🇩🇪

    In this paper we present a short overview of the new Wolfram Mathematica package intended for elementary "in-basis" tensor and differential-geometric calculations. In contrast to alternatives our package is designed to be easy-to-use, short, all-purpose, and hackable. It supports tensor contractions using Einstein notation, transformations between different bases, tensor derivative operator, expansion in basis vectors and forms, exterior derivative, and interior product.

    nucl-thcs.MScs.SChep-th+10 citations

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first authorsco-authorsvia INSPIRE