PaperPanorama

Nuclear Theory·nucl-th

Thursday·June 23, 2022

5 papers3 primary·2 cross-listed

  1. 04

    alpha-cluster structure of 18Ne

    M. Barbui🇺🇸 · A. Volya🇺🇸 · E. Aboud🇺🇸 · S. Ahn🇺🇸 · J. Bishop🇺🇸 · V.Z. Goldberg🇺🇸 · J. Hooker🇺🇸 · C.H. Hunt🇺🇸 · H. Jayatissa🇺🇸 · Tz. Kokalova🇬🇧 · E. Koshchiy🇺🇸 · S. Pirrie🇬🇧 and 6 other authors

    In this work we study alpha-clustering in 18Ne and compare it with what is known about clustering in the mirror nucleus 18O. The excitation function of 18Ne was measured in inverse kinematics from the resonant elastic scattering reaction of 14O on 4He in the excitation energy range from 8 to 17 MeV, using the active target TexAT. The analysis was performed using a multi-channel R-matrix approach. Detailed spectroscopic information is obtained from the R-matrix analysis: excitation energy of the states, spin and parity as well as partial alpha and total widths. This information is compared with theoretical models and previous data. Clustering structures appear to be robust and mostly isospin symmetric. A good correspondence was found between the levels in 18O and 18Ne. We carried out an extensive shell model analysis of the experimental data. This comparison suggests that strongly clustered states remain organized in relation to the corresponding reaction channel identified by the number of nodes in the relative alpha plus core wave function. The agreement between theory and experiment is very good and especially useful when it comes to understanding the clustering strength distribution. The comparison of the experimental data with theory shows that certain states, especially at high excitation energies, are significantly more clustered than predicted. This indicates that the structure of these states is collective and is aligned towards the corresponding alpha reaction channel.

    nucl-exnucl-thPRC(2022)·8 citations
  2. 05

    On the determination of uncertainties in parton densities

    N.T. Hunt-Smith🇦🇺 · A. Accardi🇺🇸 · W. Melnitchouk🇺🇸 · N. Sato🇺🇸 · A.W. Thomas🇦🇺 · M.J. White🇦🇺

    We review various methods used to estimate uncertainties in quantum correlation functions, such as parton distribution functions (PDFs). Using a toy model of a PDF, we compare the uncertainty estimates yielded by the traditional Hessian and data resampling methods, as well as from explicitly Bayesian analyses using nested sampling or hybrid Markov chain Monte Carlo techniques. We investigate how uncertainty bands derived from neural network approaches depend on details of the network training, and how they compare to the uncertainties obtained from more traditional methods with a specific underlying parametrization. Our results show that utilizing a neural network on a simplified example of PDF data has the potential to inflate uncertainties, in part due to the cross validation procedure that is generally used to avoid overfitting data.

    hep-phhep-exnucl-thPRD(2022)·17 citations

Affiliations

first authorsco-authorsvia INSPIRE