PaperPanorama
FAQ

Nuclear Theory·nucl-th

Monday·January 8, 2024

5 papers—2 primary·3 cross-listed

All5Primary2Cross-lists3
  1. 01

    [Submitted on 5 Jan 2024]

    Elastic p-12C scattering by using a cluster effective field theory

    Eun Jin In · Tae-Sun Park · Young-Ho Song · Seung-Woo Hong

    The elastic p-12C scattering at low energies is studied by using a cluster effective field theory (EFT), where the low-lying resonance states (s1/2, p3/2, d5/2) of 13N are treated as pertinent degrees of freedom. The low-energy constants of the Lagrangian are expressed in terms of the Coulomb-modified effective range parameters, which are determined to reproduce the experimental data for the differential cross-sections. The resulting theoretical predictions agree very well with the experimental data. The resulting theory is shown to give us almost identical phase shifts as obtained from the R-matrix approach. The role of the ground state of 13N below the threshold and the next-to-leading order in the EFT power counting are also discussed.

    Comments:
    17 pages, 6 figures
    Subjects:
    Nuclear Theory (nucl-th)
    arXiv:
    2401.02622 [pdf]
    PRC(2024)·3 citations
  2. 02

    [Submitted on 5 Jan 2024]

    Nuclear mass predictions using machine learning models

    Esra Yüksel · Derya Soydaner · Hüseyin Bahtiyar

    The exploration of nuclear mass or binding energy, a fundamental property of atomic nuclei, remains at the forefront of nuclear physics research due to limitations in experimental studies and uncertainties in model calculations, particularly when moving away from the stability line. In this work, we employ two machine learning (ML) models, Support Vector Regression (SVR) and Gaussian Process Regression (GPR), to assess their performance in predicting nuclear mass excesses using available experimental data and a physics-based feature space. We also examine the extrapolation capabilities of these models using newly measured nuclei from AME2020 and by extending our calculations beyond the training and test set regions. Our results indicate that both SVR and GPR models perform quite well within the training and test regions when informed with a physics-based feature space. Furthermore, these ML models demonstrate the ability to make reasonable predictions away from the available experimental data, offering results comparable to the model calculations. Through further refinement, these models can be used as reliable and efficient ML tools for studying nuclear properties in the future.

    Comments:
    12 pages, 8 figures
    Subjects:
    Nuclear Theory (nucl-th)
    arXiv:
    2401.02824 [pdf]
    PRC(2024)·39 citations

Affiliations

first authorsco-authorsvia INSPIRE