Bayesian uncertainty quantification for nuclear density functional theory

Mengying QIU; Zhen ZHANG

doi:10.11889/j.0253-3219.2025.hjs.48.250098

NUCLEAR TECHNIQUES, Volume. 48, Issue 5, 050006(2025)

Bayesian uncertainty quantification for nuclear density functional theory

Mengying QIU and Zhen ZHANG^*

Author Affiliations

Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai 519082, China

show less

Abstract Get PDF(in Chinese)

References(54)

[1] Editors T. Editorial: uncertainty estimates[J]. Physical Review A, 83, 040001(2011).

[2] McDonnell J D, Schunck N, Higdon D et al. Uncertainty quantification for nuclear density functional theory and information content of new measurements[J]. Physical Review Letters, 114, 122501(2015).

[3] Dobaczewski J, Nazarewicz W, Reinhard P G. Error estimates of theoretical models: a guide[J]. Journal of Physics G: Nuclear and Particle Physics, 41, 074001(2014).

[4] Piekarewicz J, Chen W C, Fattoyev F J. Information and statistics: a new paradigm in theoretical nuclear physics[J]. Journal of Physics G: Nuclear and Particle Physics, 42, 034018(2015).

[5] Drischler C, Furnstahl R J, Melendez J A et al. How well do we know the neutron-matter equation of state at the densities inside neutron stars? A Bayesian approach with correlated uncertainties[J]. Physical Review Letters, 125, 202702(2020).

[6] Everett D, Ke W, Paquet J F et al. Phenomenological constraints on the transport properties of QCD matter with data-driven model averaging[J]. Physical Review Letters, 126, 242301(2021).

[7] Neufcourt L, Cao Y C, Nazarewicz W et al. Neutron drip line in the Ca region from Bayesian model averaging[J]. Physical Review Letters, 122, 062502(2019).

[8] Giuliani P, Godbey K, Bonilla E et al. Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method[J]. Frontiers in Physics, 10, 1054524(2023).

[9] Bernhard J E, Marcy P W, Coleman-Smith C E et al. Quantifying properties of hot and dense QCD matter through systematic model-to-data comparison[J]. Physical Review C, 91, 054910(2015).

[10] Reinhard P G, Nazarewicz W. Information content of a new observable: the case of the nuclear neutron skin[J]. Physical Review C, 81, 051303(2010).

[11] Kejzlar V, Neufcourt L, Nazarewicz W et al. Statistical aspects of nuclear mass models[J]. Journal of Physics G: Nuclear and Particle Physics, 47, 094001(2020).

[12] Cirigliano V, Davoudi Z, Engel J et al. Towards precise and accurate calculations of neutrinoless double-beta decay[J]. Journal of Physics G: Nuclear and Particle Physics, 49, 120502(2022).

[13] Ashton G, Dietrich T. The use of hypermodels to understand binary neutron star collisions[J]. Nature Astronomy, 6, 961-967(2022).

[14] Chen L W, Ko C M, Li B A. Isospin-dependent properties of asymmetric nuclear matter in relativistic mean field models[J]. Physical Review C, 76, 054316(2007).

[15] Chen L W, Cai B J, Ko C M et al. Higher-order effects on the incompressibility of isospin asymmetric nuclear matter[J]. Physical Review C, 80, 014322(2009).

[16] Dutra M, Lourenço O, Sá Martins J S et al. Skyrme interaction and nuclear matter constraints[J]. Physical Review C, 85, 035201(2012).

[17] Dutra M, Lourenço O, Avancini S S et al. Relativistic mean-field hadronic models under nuclear matter constraints[J]. Physical Review C, 90, 055203(2014).

[18] Sun B Y, Bhattiprolu S, Lattimer J M. Compiled properties of nucleonic matter and nuclear and neutron star models from nonrelativistic and relativistic interactions[J]. Physical Review C, 109, 055801(2024).

[19] Xie W J, Li B A. Bayesian inference of the symmetry energy of superdense neutron-rich matter from future radius measurements of massive neutron stars[J]. The Astrophysical Journal, 899, 4(2020).

[20] Zhang Z, Chen L W. Bayesian inference of the symmetry energy and the neutron skin in 48Ca and 208Pb from CREX and PREX-2[J]. Physical Review C, 108, 024317(2023).

[21] von Toussaint U. Bayesian inference in physics[J]. Reviews of Modern Physics, 83, 943-999(2011).

[22] Burnham K P, Anderson D R[M]. Model selection and multimodel inference: a practical information-theoretic approach(2002).

[23] Qiu M Y, Cai B J, Chen L W et al. Bayesian model averaging for nuclear symmetry energy from effective proton-neutron chemical potential difference of neutron-rich nuclei[J]. Physics Letters B, 849, 138435(2024).

[24] GAO Zepeng, WANG Yongjia, LI Qingfeng et al. Application of machine learning to study the effects of quadrupole deformation on the nucleus in heavy-ion collisions at intermediate energies[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 52, 96-106(2022).

[25] GAO Zepeng, LI Qingfeng. Studies on several problems in nuclear physics by using machine learning[J]. Nuclear Techniques, 46, 080009(2023).

[26] Kass R E, Raftery A E. Bayes factors[J]. Journal of the American Statistical Association, 90, 773-795(1995).

[27] Neufcourt L, Cao Y C, Nazarewicz W et al. Bayesian approach to model-based extrapolation of nuclear observables[J]. Physical Review C, 98, 034318(2018).

[28] Rasmussen C E, Williams C K I[M]. Gaussian processes for machine learning(2006).

[29] Horowitz C J, Piekarewicz J. Neutron star structure and the neutron radius of 208Pb[J]. Physical Review Letters, 86, 5647-5650(2001).

[30] Chen W C, Piekarewicz J. Building relativistic mean field models for finite nuclei and neutron stars[J]. Physical Review C, 90, 044305(2014).

[31] Cai B J, Chen L W. Constraints on the skewness coefficient of symmetric nuclear matter within the nonlinear relativistic mean field model[J]. Nuclear Science and Techniques, 28, 185(2017).

[32] Horowitz C J, Piekarewicz J, Reed B. Insights into nuclear saturation density from parity-violating electron scattering[J]. Physical Review C, 102, 044321(2020).

[33] Bender M, Heenen P H, Reinhard P G. Self-consistent mean-field models for nuclear structure[J]. Reviews of Modern Physics, 75, 121-180(2003).

[34] Stone J R, Stone N J, Moszkowski S A. Incompressibility in finite nuclei and nuclear matter[J]. Physical Review C, 89, 044316(2014).

[35] Li B A, Han X. Constraining the neutron–proton effective mass splitting using empirical constraints on the density dependence of nuclear symmetry energy around normal density[J]. Physics Letters B, 727, 276-281(2013).

[36] Youngblood D H, Rozsa C M, Moss J M et al. Isoscalar breathing-mode state in 144Sm and 208Pb[J]. Physical Review Letters, 39, 1188-1191(1977).

[37] Howard K B, Garg U, Itoh M et al. Compressional-mode resonances in the molybdenum isotopes: Emergence of softness in open-shell nuclei near A=90[J]. Physics Letters B, 807, 135608(2020).

[38] Morris M D, Mitchell T J. Exploratory designs for computational experiments[J]. Journal of Statistical Planning and Inference, 43, 381-402(1995).

[39] Hastings W K. Monte Carlo sampling methods using Markov chains and their applications[J]. Biometrika, 57, 97-109(1970).

[40] Metropolis N, Rosenbluth A W, Rosenbluth M N et al. Equation of state calculations by fast computing machines[J]. The Journal of Chemical Physics, 21, 1087-1092(1953).

[41] Plumee M, Surer O, Wild S M et al[M]. Surmise 0.2.0 users manual, Version 0.2.0 (NAISE, 2023)(2023).

[42] Centelles M, Roca-Maza X, Viñas X et al. Nuclear symmetry energy probed by neutron skin thickness of nuclei[J]. Physical Review Letters, 102, 122502(2009).

[43] Khan E, Margueron J, Vidaña I. Constraining the nuclear equation of state at subsaturation densities[J]. Physical Review Letters, 109, 092501(2012).

[44] Fattoyev F J, Newton W G, Li B A. Quantifying correlations between isovector observables and the density dependence of the nuclear symmetry energy away from saturation density[J]. Physical Review C, 90, 022801(2014).

[45] Zhang Z, Chen L W. Constraining the symmetry energy at subsaturation densities using isotope binding energy difference and neutron skin thickness[J]. Physics Letters B, 726, 234-238(2013).

[46] Danielewicz P, Lee J. Symmetry energy II: isobaric analog states[J]. Nuclear Physics A, 922, 1-70(2014).

[47] Chen L W. Nuclear matter symmetry energy and the symmetry energy coefficient in the mass formula[J]. Physical Review C, 83, 044308(2011).

[48] Wang N, Ou L, Liu M. Nuclear symmetry energy from the Fermi-energy difference in nuclei[J]. Physical Review C, 87, 034327(2013).

[49] Wang M, Huang W J, Kondev F G et al. The AME 2020 atomic mass evaluation (II). Tables, graphs and references[J]. Chinese Physics C, 45, 030003(2021).

[50] Abril-Pla O, Andreani V, Carroll C et al. PyMC: a modern, and comprehensive probabilistic programming framework in Python[J]. PeerJ Computer Science, 9, e1516(2023).

[51] Jay W I, Neil E T. Bayesian model averaging for analysis of lattice field theory results[J]. Physical Review D, 103, 114502(2021).

[52] Lynch W G, Tsang M B. Decoding the density dependence of the nuclear symmetry energy[J]. Physics Letters B, 830, 137098(2022).

[53] Alex Brown B. Constraints on the Skyrme equations of state from properties of doubly magic nuclei[J]. Physical Review Letters, 111, 232502(2013).

[54] Trippa L, Colò G, Vigezzi E. Giant dipole resonance as a quantitative constraint on the symmetry energy[J]. Physical Review C, 77, 061304(2008).

Tools

Get Citation

Copy Citation Text

Mengying QIU, Zhen ZHANG. Bayesian uncertainty quantification for nuclear density functional theory[J]. NUCLEAR TECHNIQUES, 2025, 48(5): 050006

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Special Topics on Applications of Machine Learning in Nuclear Physics and Nuclear Data

Received: Mar. 7, 2025

Accepted: --

Published Online: Jun. 26, 2025

The Author Email: Zhen ZHANG (张振)

DOI:10.11889/j.0253-3219.2025.hjs.48.250098

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology