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Nuclear Theory·nucl-th

Wednesday·January 18, 2023

15 papers—5 primary·10 cross-listed

All15Primary5Cross-lists10
  1. 01

    QED corrections to elastic electron-nucleus scattering beyond the first-order Born approximation

    D.H.Jakubassa-Amundsen🇩🇪

    A potential for the vertex and self-energy correction is derived from the first-order Born theory. The inclusion of this potential in the Dirac equation, together with the Uehling potential for vacuum polarization, allows for a nonperturbative treatment of these QED effects within the phase-shift analysis. Investigating the 12C and 208Pb targets, a considerable deviation of the respective cross-section change from the Born results is found, which becomes larger with increasing momentum transfer. Estimates for the correction to the beam-normal spin asymmetry are also provided. For the 12C nucleus, dispersion effects are considered as well.

    nucl-th1 citation
  2. 02

    Spin-Flavor SU(6) Symmetry for Baryon-Baryon Interactions

    Makoto Oka🇯🇵

    Short-range parts of the baryon-baryon () interactions are analyzed from the spin-flavor symmetry viewpoint. Due to the Pauli principle of quarks, the symmetry structure of the wave functions is restricted at short distances. Consequently, the states with the same spin-flavor quantum numbers may be reduced into one or a few spin-flavor states. Such reduction causes repulsion and/or suppression of transitions at short distances. We show that the observed suppression of the conversion can be explained following the above argument. It is also applied to the suppression of the to conversion in the spin 1 and isospin 1/2 channel. Furthermore, the effects of the color-magnetic interaction (CMI), which prefers flavor antisymmetric states, to the Pauli-allowed states are discussed.

    nucl-thhep-lathep-ph3 citations
  3. 03

    Machine learning in nuclear physics at low and intermediate energies

    Wanbing He · Qingfeng Li · Yugang Ma · Zhongming Niu · Junchen Pei · Yingxun Zhang

    Machine learning is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing task. In this mini-review, we first briefly introduce different methodologies of the machine learning algorithms and techniques. As a snapshot of many applications by machine learning, some selected applications are presented especially for low and intermediate energy nuclear physics, which include topics on theoretical applications in nuclear structure, nuclear reactions, properties of nuclear matter as well as experimental applications in event identification/reconstruction, complex system control and firmware performance. Finally, we also give a brief summary and outlook on the possible directions of using machine learning in low-intermediate energy nuclear physics and possible improvements in ML algorithms.

    nucl-thnucl-exSCPMA(2023)·109 citations
  4. 04

    Effective field theory analysis of the Coulomb breakup of the one-neutron halo nucleus 19C

    Pierre Capel🇩🇪 · Daniel R. Phillips🇺🇸 · Andrew Andis🇺🇸 · Mirko Bagnarol🇮🇱 · Behnaz Behzadmoghaddam🇮🇷 · Francesca Bonaiti🇩🇪 · Rishabh Bubna🇩🇪 · Ylenia Capitani🇮🇹 · Pierre-Yves Duerinck🇧🇪 · Victoria Durant🇩🇪 · Niklas Döpper🇩🇪 · Aya El Boustani🇪🇸 and 20 other authors

    We analyse the Coulomb breakup of 19C measured at 67A MeV at RIKEN. We use the Coulomb-Corrected Eikonal (CCE) approximation to model the reaction and describe the one-neutron halo nucleus 19C within Halo Effective Field Theory (EFT). At leading order we obtain a fair reproduction of the measured cross section as a function of energy and angle. The description is insensitive to the choice of optical potential, as long as it accurately represents the size of 18C. It is also insensitive to the interior of the 19C wave function. Comparison between theory and experiment thus enables us to infer asymptotic properties of the ground state of 19C: these data put constraints on the one-neutron separation energy of this nucleus and, for a given binding energy, can be used to extract an asymptotic normalisation coefficient (ANC). These results are confirmed by CCE calculations employing next-to-leading order Halo EFT descriptions of 19C: at this order the results for the Coulomb breakup cross section are completely insensitive to the choice of the regulator. Accordingly, this reaction can be used to constrain the one-neutron separation energy and ANC of 19C.

    nucl-thnucl-exEPJA(2023)·5 citations
  5. 05

    Strangeness thermodynamic instabilities in hot and dense nuclear matter

    A. Lavagno🇮🇹 · D. Pigato🇮🇹

    We explore the presence of thermodynamic instabilities and, con\-se\-quen\-tly, the realization of a pure hadronic phase transition in the hot and finite baryon density nuclear matter. The analysis is performed by means of an effective relativistic mean-field model with the inclusion of hyperons, -isobars, and the lightest pseudoscalar and vector meson degrees of freedom. The Gibbs conditions on the global conservation of baryon number and zero net strangeness in symmetric nuclear matter are required. Similarly to the liquid-gas phase transition, we show that a phase transition, characterized by mechanical instabilities (due to fluctuations on the baryon number) and chemical-diffusive instabilities (due to fluctuations on the strangeness number), can take place for a finite range of -meson coupling constants, compatible with different experimental constraints. The hadronic phase transition, which presents similar features to the quark-hadron phase transition, is characterized by different strangeness content during the mixed phase and, consequently, by a sensible variation of the strange anti-particle to particle ratios.

    nucl-thEPJA(2022)·6 citations

Affiliations

first authorsco-authorsvia INSPIRE