skip to main content

DOE PAGESDOE PAGES

Title: Refining mass formulas for astrophysical applications: A Bayesian neural network approach

Authors:
;
Publication Date:
Grant/Contract Number:
FG02-92ER40750
Type:
Publisher's Accepted Manuscript
Journal Name:
Physical Review C
Additional Journal Information:
Journal Name: Physical Review C Journal Volume: 96 Journal Issue: 4; Journal ID: ISSN 2469-9985
Publisher:
American Physical Society
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
OSTI Identifier:
1398291

Utama, R., and Piekarewicz, J.. Refining mass formulas for astrophysical applications: A Bayesian neural network approach. United States: N. p., Web. doi:10.1103/PhysRevC.96.044308.
Utama, R., & Piekarewicz, J.. Refining mass formulas for astrophysical applications: A Bayesian neural network approach. United States. doi:10.1103/PhysRevC.96.044308.
Utama, R., and Piekarewicz, J.. 2017. "Refining mass formulas for astrophysical applications: A Bayesian neural network approach". United States. doi:10.1103/PhysRevC.96.044308.
@article{osti_1398291,
title = {Refining mass formulas for astrophysical applications: A Bayesian neural network approach},
author = {Utama, R. and Piekarewicz, J.},
abstractNote = {},
doi = {10.1103/PhysRevC.96.044308},
journal = {Physical Review C},
number = 4,
volume = 96,
place = {United States},
year = {2017},
month = {10}
}

Works referenced in this record:

Nuclear Ground-State Masses and Deformations
journal, March 1995
  • Moller, P.; Nix, J. R.; Myers, W. D.
  • Atomic Data and Nuclear Data Tables, Vol. 59, Issue 2, p. 185-381
  • DOI: 10.1006/adnd.1995.1002