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Nuclear Theory·nucl-th

Monday·June 12, 2023

6 papers—3 primary·3 cross-listed

All6Primary3Cross-lists3
  1. 01

    [Submitted on 9 Jun 2023]

    Resonances and scattering in microscopic cluster models with the complex-scaled generator coordinate method

    Takayuki Myo · Hiroki Takemoto

    The generator coordinate method of a microscopic cluster model is developed to treat the resonance and scattering of nuclear clusters with complex scaling. We consistently derive the formulation of the complex scaling for the microscopic cluster model, in which only the relative motions between clusters are transformed in the generator coordinate wave function. We also reveal the applicability of this method to the cluster wave function. Furthermore, we demonstrate this framework in the 2 system of Be and obtain the solutions of resonance and non-resonant continuum states. Using these solutions, we calculate the level density, which brings the phase shifts of the cluster-cluster scattering. This work becomes the foundation in the description of the multi-cluster scattering states of nuclei in a microscopic framework with complex scaling.

    Comments:
    9 pages, 9 figures
    Subjects:
    Nuclear Theory (nucl-th)
    arXiv:
    2306.05660 [pdf]
    PRC(2023)·14 citations
  2. 02

    [Submitted on 9 Jun 2023]

    Quantum kinetic theory with interactions for massive vector bosons

    David Wagner🇩🇪 · Nora Weickgenannt🇩🇪 · Enrico Speranza🇺🇸

    We present a derivation of quantum kinetic theory for massive spin-1 particles from the Wigner-function formalism up to first order in an -expansion, including a general interaction term. Both local and nonlocal contributions are computed in a covariant fashion. It is shown that, up to first order in , the collision term takes the same form as in the case of spin-1/2 particles.

    Comments:
    22 pages, no figures
    Subjects:
    Nuclear Theory (nucl-th)
    arXiv:
    2306.05936 [pdf]
    PRD(2023)·20 citations
  3. 03

    [Submitted on 9 Jun 2023]

    NuCLR: Nuclear Co-Learned Representations

    Ouail Kitouni · Niklas Nolte · Sokratis Trifinopoulos · Subhash Kantamneni · Mike Williams

    We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii. The model is trained using a multi-task approach with shared representations and obtains state-of-the-art performance, achieving levels of precision that are crucial for understanding fundamental phenomena in nuclear (astro)physics. We also report an intriguing finding that the learned representation of NuCLR exhibits the prominent emergence of crucial aspects of the nuclear shell model, namely the shell structure, including the well-known magic numbers, and the Pauli Exclusion Principle. This suggests that the model is capable of capturing the underlying physical principles and that our approach has the potential to offer valuable insights into nuclear theory.

    Comments:
    7 pages, 5 figures. Accepted after peer review at the ICML 2023 1st workshop on Synergy of Scientific and Machine Learning Modeling (SynS & ML)
    Subjects:
    Nuclear Theory (nucl-th); Machine Learning (cs.LG); Nuclear Experiment (nucl-ex)
    arXiv:
    2306.06099 [pdf]
    2 citations

Affiliations

first authorsco-authorsvia INSPIRE