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Title: Constraining Fission Yields Using Machine Learning

Abstract

Having accurate measurements of fission observables is important for a variety of applications, ranging from energy to non-proliferation, defense to astrophysics. Because not all of these data can be measured, it is necessary to be able to accurately calculate these observables as well. In this work, we exploit Monte Carlo and machine learning techniques to reproduce mass and kinetic energy yields, for phenomenological models and in a model-free way. Finally, we begin with the spontaneous fission of 252Cf, where there is abundant experimental data, to validate our approach, with the ultimate goal of creating a global yield model in order to predict quantities where data are not currently available.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1529541
Report Number(s):
LA-UR-18-30827
Journal ID: ISSN 2100-014X
Grant/Contract Number:  
89233218CNA000001; AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
EPJ Web of Conferences
Additional Journal Information:
Journal Volume: 211; Journal ID: ISSN 2100-014X
Publisher:
EDP Sciences
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Citation Formats

Lovell, Amy Elizabeth, Mohan, Arvind Thanam, Talou, Patrick, and Chertkov, Michael. Constraining Fission Yields Using Machine Learning. United States: N. p., 2019. Web. doi:10.1051/epjconf/201921104006.
Lovell, Amy Elizabeth, Mohan, Arvind Thanam, Talou, Patrick, & Chertkov, Michael. Constraining Fission Yields Using Machine Learning. United States. doi:10.1051/epjconf/201921104006.
Lovell, Amy Elizabeth, Mohan, Arvind Thanam, Talou, Patrick, and Chertkov, Michael. Wed . "Constraining Fission Yields Using Machine Learning". United States. doi:10.1051/epjconf/201921104006. https://www.osti.gov/servlets/purl/1529541.
@article{osti_1529541,
title = {Constraining Fission Yields Using Machine Learning},
author = {Lovell, Amy Elizabeth and Mohan, Arvind Thanam and Talou, Patrick and Chertkov, Michael},
abstractNote = {Having accurate measurements of fission observables is important for a variety of applications, ranging from energy to non-proliferation, defense to astrophysics. Because not all of these data can be measured, it is necessary to be able to accurately calculate these observables as well. In this work, we exploit Monte Carlo and machine learning techniques to reproduce mass and kinetic energy yields, for phenomenological models and in a model-free way. Finally, we begin with the spontaneous fission of252Cf, where there is abundant experimental data, to validate our approach, with the ultimate goal of creating a global yield model in order to predict quantities where data are not currently available.},
doi = {10.1051/epjconf/201921104006},
journal = {EPJ Web of Conferences},
number = ,
volume = 211,
place = {United States},
year = {2019},
month = {6}
}

Journal Article:
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