skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Quantified limits of the nuclear landscape

Abstract

The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses well beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a wide variety of applications, including the modeling of stellar nucleosynthesis. We use microscopic nuclear global mass models, current mass data, and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines. Here, we apply nuclear density functional theory with several energy density functionals. We also consider two global mass models often used in astrophysical nucleosynthesis simulations. To account for uncertainties, Bayesian Gaussian processes are trained on the separation-energy residuals for each individual model, and the resulting predictions are combined via Bayesian model averaging. This framework allows to account for systematic and statistical uncertainties and propagate them to extrapolative predictions. We establish and characterize the drip-line regions where the probability that the nucleus is particle- bound decreases from 1 to 0.more » In these regions, we provide quantified predictions for one- and two-nucleon separation energies. According to our Bayesian model averaging analysis, 7759 nuclei with Z ≤ 119 have a probability of existence ≥ 0.5. The extrapolation results obtained in this study will be put through stringent tests when new experimental information on existence and masses of exotic nuclei becomes available. In this respect, the quantified landscape of nuclear existence obtained in this study should be viewed as a dynamical prediction that will be fine-tuned when new experimental information and improved global mass models become available.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Michigan State Univ., East Lansing, MI (United States)
  2. Univ. Libre de Bruxelles, Brussels (Belgium)
Publication Date:
Research Org.:
Michigan State Univ., East Lansing, MI (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP)
OSTI Identifier:
1615731
Alternate Identifier(s):
OSTI ID: 1631894
Grant/Contract Number:  
SC0013365; SC0008511; NA0002847; SC0018083; NA0003885; DOE-NA0003885
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review C
Additional Journal Information:
Journal Volume: 101; Journal Issue: 4; Journal ID: ISSN 2469-9985
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; binding energy & masses; learning; nuclear density functional theory; nuclear structure & decays; Bayesian methods; machine learning; networks; nuclear physics

Citation Formats

Neufcourt, Léo, Cao, Yuchen, Giuliani, Samuel A., Nazarewicz, Witold, Olsen, Erik, and Tarasov, Oleg B. Quantified limits of the nuclear landscape. United States: N. p., 2020. Web. https://doi.org/10.1103/PhysRevC.101.044307.
Neufcourt, Léo, Cao, Yuchen, Giuliani, Samuel A., Nazarewicz, Witold, Olsen, Erik, & Tarasov, Oleg B. Quantified limits of the nuclear landscape. United States. https://doi.org/10.1103/PhysRevC.101.044307
Neufcourt, Léo, Cao, Yuchen, Giuliani, Samuel A., Nazarewicz, Witold, Olsen, Erik, and Tarasov, Oleg B. Tue . "Quantified limits of the nuclear landscape". United States. https://doi.org/10.1103/PhysRevC.101.044307. https://www.osti.gov/servlets/purl/1615731.
@article{osti_1615731,
title = {Quantified limits of the nuclear landscape},
author = {Neufcourt, Léo and Cao, Yuchen and Giuliani, Samuel A. and Nazarewicz, Witold and Olsen, Erik and Tarasov, Oleg B.},
abstractNote = {The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses well beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a wide variety of applications, including the modeling of stellar nucleosynthesis. We use microscopic nuclear global mass models, current mass data, and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines. Here, we apply nuclear density functional theory with several energy density functionals. We also consider two global mass models often used in astrophysical nucleosynthesis simulations. To account for uncertainties, Bayesian Gaussian processes are trained on the separation-energy residuals for each individual model, and the resulting predictions are combined via Bayesian model averaging. This framework allows to account for systematic and statistical uncertainties and propagate them to extrapolative predictions. We establish and characterize the drip-line regions where the probability that the nucleus is particle- bound decreases from 1 to 0. In these regions, we provide quantified predictions for one- and two-nucleon separation energies. According to our Bayesian model averaging analysis, 7759 nuclei with Z ≤ 119 have a probability of existence ≥ 0.5. The extrapolation results obtained in this study will be put through stringent tests when new experimental information on existence and masses of exotic nuclei becomes available. In this respect, the quantified landscape of nuclear existence obtained in this study should be viewed as a dynamical prediction that will be fine-tuned when new experimental information and improved global mass models become available.},
doi = {10.1103/PhysRevC.101.044307},
journal = {Physical Review C},
number = 4,
volume = 101,
place = {United States},
year = {2020},
month = {4}
}

Journal Article:

Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach
journal, January 2016


First Direct Mass Measurements of Nuclides around Z = 100 with a Multireflection Time-of-Flight Mass Spectrograph
journal, April 2018


Future Opportunities at the Facility for Rare Isotope Beams
journal, January 2018


Nuclear deformation in the A 100 region: Comparison between new masses and mean-field predictions
journal, July 2017


Neutron Drip Line in the Ca Region from Bayesian Model Averaging
journal, February 2019


The limits of the nuclear landscape
journal, June 2012

  • Erler, Jochen; Birge, Noah; Kortelainen, Markus
  • Nature, Vol. 486, Issue 7404
  • DOI: 10.1038/nature11188

Hartree-Fock-Bogolyubov description of nuclei near the neutron-drip line
journal, June 1984


Nuclear energy density optimization: Shell structure
journal, May 2014


Dawning of the N = 32 Shell Closure Seen through Precision Mass Measurements of Neutron-Rich Titanium Isotopes
journal, February 2018


Performance of the Levenberg–Marquardt neural network approach in nuclear mass prediction
journal, March 2017

  • Zhang, Hai Fei; Wang, Li Hao; Yin, Jing Peng
  • Journal of Physics G: Nuclear and Particle Physics, Vol. 44, Issue 4
  • DOI: 10.1088/1361-6471/aa5d78

Binding Energy of Cu 79 : Probing the Structure of the Doubly Magic Ni 78 from Only One Proton Away
journal, November 2017


First Gogny-Hartree-Fock-Bogoliubov Nuclear Mass Model
journal, June 2009


David Draper and E. I. George, and a rejoinder by the authors
journal, November 1999

  • Volinsky, Chris T.; Raftery, Adrian E.; Madigan, David
  • Statistical Science, Vol. 14, Issue 4
  • DOI: 10.1214/ss/1009212519

The AME2016 atomic mass evaluation (II). Tables, graphs and references
journal, March 2017


The Ame2003 atomic mass evaluation
journal, December 2003


Impact of Nuclear Mass Uncertainties on the r Process
journal, March 2016


The impact of individual nuclear properties on r -process nucleosynthesis
journal, January 2016

  • Mumpower, M. R.; Surman, R.; McLaughlin, G. C.
  • Progress in Particle and Nuclear Physics, Vol. 86
  • DOI: 10.1016/j.ppnp.2015.09.001

LISE++: Radioactive beam production with in-flight separators
journal, October 2008

  • Tarasov, O. B.; Bazin, D.
  • Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol. 266, Issue 19-20
  • DOI: 10.1016/j.nimb.2008.05.110

Nuclear ground-state masses and deformations: FRDM(2012)
journal, May 2016


Variations on a theme by Skyrme: A systematic study of adjustments of model parameters
journal, March 2009


Precision Mass Measurements of Neutron-Rich Neodymium and Samarium Isotopes and Their Role in Understanding Rare-Earth Peak Formation
journal, June 2018


Towards a better parametrisation of Skyrme-like effective forces: A critical study of the SkM force
journal, September 1982


Nuclear mass measurements with radioactive ion beams
journal, April 2019


Location of the Neutron Dripline at Fluorine and Neon
journal, November 2019


Neutron drip line: Single-particle degrees of freedom and pairing properties as sources of theoretical uncertainties
journal, January 2015


Masses of neutron-rich Ni and Cu isotopes and the shell closure at Z = 28 , N = 40
journal, October 2007


Structure of even-even nuclei using a mapped collective Hamiltonian and the D1S Gogny interaction
journal, January 2010


New Skyrme effective forces for supernovae and neutron rich nuclei
journal, January 1995


Positioning the neutron drip line and the r-process paths in the nuclear landscape
journal, September 2015


Nuclear energy density optimization: Large deformations
journal, February 2012


Discovery of Ca 60 and Implications For the Stability of Ca 70
journal, July 2018


Radioactive decays at limits of nuclear stability
journal, April 2012


Magic Nature of Neutrons in Ca 54 : First Mass Measurements of Ca 55 57
journal, July 2018


Refining mass formulas for astrophysical applications: A Bayesian neural network approach
journal, October 2017


Observation of new neutron-rich Mn, Fe, Co, Ni, and Cu isotopes in the vicinity of Ni 78
journal, May 2017


Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
journal, January 2020


Energy density functional for nuclei and neutron stars
journal, April 2013


Nuclear energy density optimization
journal, August 2010


The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory
journal, May 2018


Self-consistent mean-field models for nuclear structure
journal, January 2003

  • Bender, Michael; Heenen, Paul-Henri; Reinhard, Paul-Gerhard
  • Reviews of Modern Physics, Vol. 75, Issue 1
  • DOI: 10.1103/RevModPhys.75.121

Nuclear landscape in covariant density functional theory
journal, November 2013


New Kohn-Sham density functional based on microscopic nuclear and neutron matter equations of state
journal, June 2013


LISE++ development: Application to projectile fission at relativistic energies
journal, May 2005


Facility for Rare Isotope Beams Update for Nuclear Physics News
journal, April 2017


Modified empirical parametrization of fragmentation cross sections
journal, February 2000


Validating neural-network refinements of nuclear mass models
journal, January 2018


Bayesian approach to model-based extrapolation of nuclear observables
journal, September 2018


Bayesian Model Selection and Model Averaging
journal, March 2000


Nuclear mass systematics using neural networks
journal, November 2004


r -process nucleosynthesis: connecting rare-isotope beam facilities with the cosmos
journal, July 2019

  • Horowitz, C. J.; Arcones, A.; Côté, B.
  • Journal of Physics G: Nuclear and Particle Physics, Vol. 46, Issue 8
  • DOI: 10.1088/1361-6471/ab0849

β-Stability Line and Liquid-Drop Mass Formulas
journal, April 1971


Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
journal, March 2015