Physically interpretable machine learning for nuclear masses

Mumpower, M. R.; Sprouse, T. M.; Lovell, A. E.; Mohan, A. T.

doi:10.1103/PhysRevC.106.L021301

Title: Physically interpretable machine learning for nuclear masses

Journal Article · 31 July 2022 · Physical Review C

DOI: https://doi.org/10.1103/PhysRevC.106.L021301 · OSTI ID:1879249

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Nuclear Physics (NP)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1879249

Report Number(s):: LA-UR--22-21855; L021301

Journal Information:: Physical Review C, Journal Name: Physical Review C Journal Issue: 2 Vol. 106; ISSN PRVCAN; ISSN 2469-9985

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (45)

Zur Theorie der Kernmassen Weizs�cker, C. F. v. Zeitschrift f�r Physik, Vol. 96, Issue 7-8 https://doi.org/10.1007/BF01337700	journal	July 1935
Mass predictions from the Garvey-Kelson mass relations Jänecke, J.; Masson, P. J. Atomic Data and Nuclear Data Tables, Vol. 39, Issue 2 https://doi.org/10.1016/0092-640X(88)90028-9	journal	July 1988
Nuclear ground-state masses and deformations: FRDM(2012) Möller, P.; Sierk, A. J.; Ichikawa, T. Atomic Data and Nuclear Data Tables, Vol. 109-110 https://doi.org/10.1016/j.adt.2015.10.002	journal	May 2016
Mass excess estimations using artificial neural networks Özdoğan, H.; Üncü, Y. A.; Şekerci, M. Applied Radiation and Isotopes, Vol. 184 https://doi.org/10.1016/j.apradiso.2022.110162	journal	June 2022
How good are the Garvey–Kelson predictions of nuclear masses? Morales, Irving O.; López Vieyra, J. C.; Hirsch, J. G. Nuclear Physics A, Vol. 828, Issue 1-2 https://doi.org/10.1016/j.nuclphysa.2009.07.001	journal	September 2009
Plans for the Facility for Rare Isotope Beams Thoennessen, M. Nuclear Physics A, Vol. 834, Issue 1-4 https://doi.org/10.1016/j.nuclphysa.2010.01.125	journal	March 2010
Modeling the Emission of Prompt Fission γ Rays for Fundamental Physics and Applications Talou, Patrick; Stetcu, Ionel; Kawano, Toshihiko Physics Procedia, Vol. 59 https://doi.org/10.1016/j.phpro.2014.10.013	journal	January 2014
Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects Niu, Z. M.; Liang, H. Z. Physics Letters B, Vol. 778 https://doi.org/10.1016/j.physletb.2018.01.002	journal	March 2018
The impact of individual nuclear properties on r -process nucleosynthesis Mumpower, M. R.; Surman, R.; McLaughlin, G. C. Progress in Particle and Nuclear Physics, Vol. 86 https://doi.org/10.1016/j.ppnp.2015.09.001	journal	January 2016
A review of multi-objective optimization: Methods and its applications Gunantara, Nyoman Cogent Engineering, Vol. 5, Issue 1 https://doi.org/10.1080/23311916.2018.1502242	journal	January 2018
The evolution of nuclear structure: the scheme and related correlations Casten, R. F.; Zamfir, N. V. Journal of Physics G: Nuclear and Particle Physics, Vol. 22, Issue 11 https://doi.org/10.1088/0954-3899/22/11/002	journal	November 1996
Calibration of energy density functionals with deformed nuclei Schunck, N.; O’Neal, J.; Grosskopf, M. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 7 https://doi.org/10.1088/1361-6471/ab8745	journal	June 2020
Quantifying uncertainties on fission fragment mass yields with mixture density networks Lovell, A. E.; Mohan, A. T.; Talou, P. Journal of Physics G: Nuclear and Particle Physics, Vol. 47, Issue 11 https://doi.org/10.1088/1361-6471/ab9f58	journal	September 2020
Extrapolating from neural network models: a cautionary tale Pastore, A.; Carnini, M. Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Issue 8 https://doi.org/10.1088/1361-6471/abf08a	journal	June 2021
The neutron star outer crust equation of state: a machine learning approach Murarka, Utsav; Banerjee, Kinjal; Malik, Tuhin Journal of Cosmology and Astroparticle Physics, Vol. 2022, Issue 01 https://doi.org/10.1088/1475-7516/2022/01/045	journal	January 2022
The AME2016 atomic mass evaluation (II). Tables, graphs and references Wang, Meng; Audi, G.; Kondev, F. G. Chinese Physics C, Vol. 41, Issue 3 https://doi.org/10.1088/1674-1137/41/3/030003	journal	March 2017
The NUBASE2020 evaluation of nuclear physics properties * Kondev, F. G.; Wang, M.; Huang, W. J. Chinese Physics C, Vol. 45, Issue 3 https://doi.org/10.1088/1674-1137/abddae	journal	March 2021
The AME 2020 atomic mass evaluation (II). Tables, graphs and references* Wang, Meng; Huang, W. J.; Kondev, F. G. Chinese Physics C, Vol. 45, Issue 3 https://doi.org/10.1088/1674-1137/abddaf	journal	March 2021
Quantifying correlated truncation errors in effective field theory Melendez, J. A.; Furnstahl, R. J.; Phillips, D. R. Physical Review C, Vol. 100, Issue 4 https://doi.org/10.1103/PhysRevC.100.044001	journal	October 2019
Extrapolation of nuclear structure observables with artificial neural networks Jiang, W. G.; Hagen, G.; Papenbrock, T. Physical Review C, Vol. 100, Issue 5 https://doi.org/10.1103/PhysRevC.100.054326	journal	November 2019
Quantified limits of the nuclear landscape Neufcourt, Léo; Cao, Yuchen; Giuliani, Samuel A. Physical Review C, Vol. 101, Issue 4 https://doi.org/10.1103/PhysRevC.101.044307	journal	April 2020
Propagation of statistical uncertainties of Skyrme mass models to simulations of r -process nucleosynthesis Sprouse, T. M.; Navarro Perez, R.; Surman, R. Physical Review C, Vol. 101, Issue 5 https://doi.org/10.1103/PhysRevC.101.055803	journal	May 2020
Machine-learning-based inversion of nuclear responses Raghavan, Krishnan; Balaprakash, Prasanna; Lovato, Alessandro Physical Review C, Vol. 103, Issue 3 https://doi.org/10.1103/PhysRevC.103.035502	journal	March 2021
Machine-learning approach to finite-size effects in systems with strongly interacting fermions Ismail, Nawar; Gezerlis, Alexandros Physical Review C, Vol. 104, Issue 5 https://doi.org/10.1103/PhysRevC.104.055802	journal	November 2021
Learning correlations in nuclear masses using neural networks Sharma, Aman; Gandhi, A.; Kumar, Ajay Physical Review C, Vol. 105, Issue 3 https://doi.org/10.1103/PhysRevC.105.L031306	journal	March 2022
Nuclear masses learned from a probabilistic neural network Lovell, A. E.; Mohan, A. T.; Sprouse, T. M. Physical Review C, Vol. 106, Issue 1 https://doi.org/10.1103/PhysRevC.106.014305	journal	July 2022
Garvey-Kelson relations and the new nuclear mass tables Barea, J.; Frank, A.; Hirsch, J. G. Physical Review C, Vol. 77, Issue 4 https://doi.org/10.1103/PhysRevC.77.041304	journal	April 2008
Deep crustal heating in a multicomponent accreted neutron star crust Steiner, Andrew W. Physical Review C, Vol. 85, Issue 5 https://doi.org/10.1103/PhysRevC.85.055804	journal	May 2012
Neutrino-pair emission from hot nuclei during stellar collapse Misch, G. Wendell; Brown, B. Alex; Fuller, George M. Physical Review C, Vol. 88, Issue 1 https://doi.org/10.1103/PhysRevC.88.015807	journal	July 2013
Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach Utama, R.; Piekarewicz, J.; Prosper, H. B. Physical Review C, Vol. 93, Issue 1 https://doi.org/10.1103/PhysRevC.93.014311	journal	January 2016
Statistical Hauser-Feshbach theory with width-fluctuation correction including direct reaction channels for neutron-induced reactions at low energies Kawano, T.; Capote, R.; Hilaire, S. Physical Review C, Vol. 94, Issue 1 https://doi.org/10.1103/PhysRevC.94.014612	journal	July 2016
Bayesian approach to model-based extrapolation of nuclear observables Neufcourt, Léo; Cao, Yuchen; Nazarewicz, Witold Physical Review C, Vol. 98, Issue 3 https://doi.org/10.1103/PhysRevC.98.034318	journal	September 2018
Deep learning: Extrapolation tool for ab initio nuclear theory Negoita, Gianina Alina; Vary, James P.; Luecke, Glenn R. Physical Review C, Vol. 99, Issue 5 https://doi.org/10.1103/PhysRevC.99.054308	journal	May 2019
First Gogny-Hartree-Fock-Bogoliubov Nuclear Mass Model Goriely, S.; Hilaire, S.; Girod, M. Physical Review Letters, Vol. 102, Issue 24 https://doi.org/10.1103/PhysRevLett.102.242501	journal	June 2009
Precision Mass Measurements of Neutron-Rich Neodymium and Samarium Isotopes and Their Role in Understanding Rare-Earth Peak Formation Orford, R.; Vassh, N.; Clark, J. A. Physical Review Letters, Vol. 120, Issue 26 https://doi.org/10.1103/PhysRevLett.120.262702	journal	June 2018
Neutron Drip Line in the Ca Region from Bayesian Model Averaging Neufcourt, Léo; Cao, Yuchen; Nazarewicz, Witold Physical Review Letters, Vol. 122, Issue 6 https://doi.org/10.1103/PhysRevLett.122.062502	journal	February 2019
Generation of Fragment Angular Momentum in Fission Randrup, Jørgen; Vogt, Ramona Physical Review Letters, Vol. 127, Issue 6 https://doi.org/10.1103/PhysRevLett.127.062502	journal	August 2021
Valence p - n interactions and the development of collectivity in heavy nuclei Casten, R. F.; Brenner, D. S.; Haustein, P. E. Physical Review Letters, Vol. 58, Issue 7 https://doi.org/10.1103/PhysRevLett.58.658	journal	February 1987
Current nuclear data needs for applications Kolos, Karolina; Sobes, Vladimir; Vogt, Ramona Physical Review Research, Vol. 4, Issue 2 https://doi.org/10.1103/PhysRevResearch.4.021001	journal	April 2022
Set of Nuclear-Mass Relations and a Resultant Mass Table Garvey, G. T.; Gerace, W. J.; Jaffe, R. L. Reviews of Modern Physics, Vol. 41, Issue 4 https://doi.org/10.1103/RevModPhys.41.S1	journal	October 1969
The mean-field theory of nuclear structure and dynamics Negele, J. W. Reviews of Modern Physics, Vol. 54, Issue 4 https://doi.org/10.1103/RevModPhys.54.913	journal	October 1982
Recent trends in the determination of nuclear masses Lunney, D.; Pearson, J. M.; Thibault, C. Reviews of Modern Physics, Vol. 75, Issue 3 https://doi.org/10.1103/RevModPhys.75.1021	journal	August 2003
Our Future Nuclear Data Needs Bernstein, Lee A.; Brown, David A.; Koning, Arjan J. Annual Review of Nuclear and Particle Science, Vol. 69, Issue 1 https://doi.org/10.1146/annurev-nucl-101918-023708	journal	October 2019
A New Mass Model for Nuclear Astrophysics: Crossing 200 keV Accuracy Shelley, Matthew; Pastore, Alessandro Universe, Vol. 7, Issue 5 https://doi.org/10.3390/universe7050131	journal	May 2021
Modeling Kilonova Light Curves: Dependence on Nuclear Inputs Zhu, Y. L.; Lund, K. A.; Barnes, J. The Astrophysical Journal, Vol. 906, Issue 2 https://doi.org/10.3847/1538-4357/abc69e	journal	January 2021

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