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Nuclear Theory·nucl-th

Thursday·January 21, 2021

7 papers—5 primary·2 cross-listed

All7Primary5Cross-lists2
  1. 01

    Tasting Nuclear Pasta Made with Classical Molecular Dynamics Simulations

    Bao-An Li🇺🇸

    Nuclear clusters or voids in the inner crust of neutron stars were predicted to have various shapes collectively nicknamed nuclear pasta. The recent review in Ref. \cite{Lopez1} by López, Dorso and Frank summarized their systematic investigations into properties especially the morphological and thermodynamical phase transitions of the nuclear pasta within a Classical Molecular Dynamics model, providing further stimuli to find more observational evidences of the predicted nuclear pasta in neutron stars.

    nucl-thastro-ph.HEnucl-exFront.Phys.(Beijing)(2021)·2 citations
  2. 02

    Editorial: The Future of Nuclear Structure: Challenges and Opportunities in the Microscopic Description of Nuclei

    Luigi Coraggio · Saori Pastore · Carlo Barbieri

    The past two decades have witnessed tremendous progress in the microscopic description of atomic nuclei. The Topical Review `The Future of Nuclear Structure' aims at summarizing the current state-of-the-art microscopic calculations in Nuclear Theory and to give a useful reference for young researches who wish to learn more about this exciting discipline.

    nucl-thFront.in Phys.(2021)·10 citations
  4. 04

    Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation

    Yuki Fujimoto🇯🇵 · Kenji Fukushima🇯🇵 · Koichi Murase🇨🇳

    We discuss deep learning inference for the neutron star equation of state (EoS) using the real observational data of the mass and the radius. We make a quantitative comparison between the conventional polynomial regression and the neural network approach for the EoS parametrization. For our deep learning method to incorporate uncertainties in observation, we augment the training data with noise fluctuations corresponding to observational uncertainties. Deduced EoSs can accommodate a weak first-order phase transition, and we make a histogram for likely first-order regions. We also find that our observational data augmentation has a byproduct to tame the overfitting behavior. To check the performance improved by the data augmentation, we set up a toy model as the simplest inference problem to recover a double-peaked function and monitor the validation loss. We conclude that the data augmentation could be a useful technique to evade the overfitting without tuning the neural network architecture such as inserting the dropout.

    nucl-thastro-ph.HEastro-ph.IMcs.LG+1JHEP(2021)·76 citations
  5. 05

    A mathematical model to describe the alpha dose rate from a UO2 surface

    Angus Siberry · David Hambley · Anna Adamska · Ross Springell

    A model to determine the dose rate of a planar alpha-emitting surface, has been developed. The approach presented is a computationally efficient mathematical model using stopping range data from the Stopping Ranges of Ions in Matter (SRIM) software. The alpha dose rates as a function of distance from irradiated UO2 spent fuel surfaces were produced for bench-marking with previous modelling attempts. This method is able to replicate a Monte Carlo (MCNPX) study of an irradiated UO2 fuel surface within 0.6 % of the resulting total dose rate and displays a similar dose profile.

    nucl-thRadiat.Phys.Chem.(2021)·1 citation

Affiliations

first authorsco-authorsvia INSPIRE