Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

NUCLEAR TECHNIQUES, Volume. 48, Issue 5, 050003(2025)

Nucleon separation energy predictions with the Kernel ridge regression

Yueying GUO, Xiangqi TANG, Huixin LIU, and Xinhui WU*
Author Affiliations
  • College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (5)
    Equations (0)
    References (66)
    Tools
    Paper Information
    Topics
    References(66)

    [1] Yamaguchi T, Koura H, Litvinov Y A et al. Masses of exotic nuclei[J]. Progress in Particle and Nuclear Physics, 120, 103882(2021).

    [2] Monteagudo B, Marqués F M, Gibelin J et al. Mass, spectroscopy, and two-neutron decay of 16Be[J]. Physical Review Letters, 132, 082501(2024).

    [3] Mumpower M R, Surman R, McLaughlin G C et al. The impact of individual nuclear properties on r-process nucleosynthesis[J]. Progress in Particle and Nuclear Physics, 86, 86-126(2016).

    [4] Jiang X F, Wu X H, Zhao P W. Sensitivity study of r-process abundances to nuclear masses[J]. The Astrophysical Journal, 915, 29(2021).

    [5] Wu X H, Zhao P W, Zhang S Q et al. High-precision nuclear chronometer for the cosmos[J]. The Astrophysical Journal, 941, 152(2022).

    [6] Hager U, Eronen T, Hakala J et al. First precision mass measurements of refractory fission fragments[J]. Physical Review Letters, 96, 042504(2006).

    [7] de Roubin A, Atanasov D, Blaum K et al. Nuclear deformation in the A≈100 region: comparison between new masses and mean-field predictions[J]. Physical Review C, 96, 014310(2017).

    [8] Minaya Ramirez E, Ackermann D, Blaum K et al. Direct mapping of nuclear shell effects in the heaviest elements[J]. Science, 337, 1207-1210(2012).

    [9] Wienholtz F, Beck D, Blaum K et al. Masses of exotic calcium isotopes pin down nuclear forces[J]. Nature, 498, 346-349(2013).

    [10] Alex Brown B. New Skyrme interaction for normal and exotic nuclei[J]. Physical Review C, 58, 220-231(1998).

    [11] Lalazissis G A, Nikšić T, Vretenar D et al. New relativistic mean-field interaction with density-dependent meson-nucleon couplings[J]. Physical Review C, 71, 024312(2005).

    [12] Zhao P W, Li Z P, Yao J M et al. New parametrization for the nuclear covariant energy density functional with a point-coupling interaction[J]. Physical Review C, 82, 054319(2010).

    [13] Bethe H A. Energy production in stars[J]. Physical Review, 55, 434-456(1939).

    [14] Burbidge E M, Burbidge G R, Fowler W A et al. Synthesis of the elements in stars[J]. Reviews of Modern Physics, 29, 547-650(1957).

    [15] Wang M, Audi G, Wapstra A H et al. The Ame2012 atomic mass evaluation[J]. Chinese Physics C, 36, 1603-2014(2012).

    [16] 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).

    [17] Weizsäcker C F V. Zur theorie der kernmassen[J]. Zeitschrift Für Physik, 96, 431-458(1935).

    [18] Möller P, Sierk A J, Ichikawa T et al. Nuclear ground-state masses and deformations: FRDM(2012)[J]. Atomic Data and Nuclear Data Tables, 109, 1-204(2016).

    [19] Wang N, Liu M, Wu X Z et al. Surface diffuseness correction in global mass formula[J]. Physics Letters B, 734, 215-219(2014).

    [20] Pearson J M, Nayak R C, Goriely S. Nuclear mass formula with Bogolyubov-enhanced shell-quenching: application to r-process[J]. Physics Letters B, 387, 455-459(1996).

    [21] Koura H, Tachibana T, Uno M et al. Nuclidic mass formula on a spherical basis with an improved even-odd term[J]. Progress of Theoretical Physics, 113, 305-325(2005).

    [22] LI Tao, LI Chunqing, ZHOU Houbing et al. Test of nuclear mass models[J]. Acta Physica Sinica, 70, 91-98(2021).

    [23] Goriely S, Chamel N, Pearson J M. Hartree-Fock-Bogoliubov nuclear mass model with 0.50 MeV accuracy based on standard forms of Skyrme and pairing functionals[J]. Physical Review C, 88, 061302(2013).

    [24] Geng L, Toki H, Meng J. Masses, deformations and charge radii: nuclear ground-state properties in the relativistic mean field model[J]. Progress of Theoretical Physics, 113, 785-800(2005).

    [25] Erler J, Birge N, Kortelainen M et al. The limits of the nuclear landscape[J]. Nature, 486, 509-512(2012).

    [26] Afanasjev A V, Agbemava S E, Ray D et al. Nuclear landscape in covariant density functional theory[J]. Physics Letters B, 726, 680-684(2013).

    [27] Lu K Q, Li Z X, Li Z P et al. Global study of beyond-mean-field correlation energies in covariant energy density functional theory using a collective Hamiltonian method[J]. Physical Review C, 91, 027304(2015).

    [28] Xia X W, Lim Y, Zhao P W et al. The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory[J]. Atomic Data and Nuclear Data Tables, 121, 1-215(2018).

    [29] Zhang K Y, Cheoun M K, Choi Y B et al. Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum I: Even-even nuclei[J]. Atomic Data and Nuclear Data Tables, 144, 101488(2022).

    [30] Pan C, Cheoun M K, Choi Y B et al. Deformed relativistic Hartree-Bogoliubov theory in continuum with a point-coupling functional II. Examples of odd Nd isotopes[J]. Physical Review C, 106, 014316(2022).

    [31] Guo P, Cao X J, Chen K M et al. Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum II: Even- Z nuclei[J]. Atomic Data and Nuclear Data Tables, 158, 101661(2024).

    [32] Kirson M W. Mutual influence of terms in a semi-empirical mass formula[J]. Nuclear Physics A, 798, 29-60(2008).

    [33] Carleo G, Cirac I, Cranmer K et al. Machine learning and the physical sciences[J]. Reviews of Modern Physics, 91, 045002(2019).

    [34] Carleo G, Troyer M. Solving the quantum many-body problem with artificial neural networks[J]. Science, 355, 602-606(2017).

    [35] Boehnlein A, Diefenthaler M, Sato N et al. Colloquium: Machine learning in nuclear physics[J]. Reviews of Modern Physics, 94, 031003(2022).

    [36] He W B, Li Q F, Ma Y G et al. Machine learning in nuclear physics at low and intermediate energies[J]. Science China Physics, Mechanics & Astronomy, 66, 282001(2023).

    [37] Ma Y G, Pang L G, Wang R et al. Phase transition study meets machine learning[J]. Chinese Physics Letters, 40, 122101(2023).

    [38] Zhou K, Wang L X, Pang L G et al. Exploring QCD matter in extreme conditions with Machine Learning[J]. Progress in Particle and Nuclear Physics, 135, 104084(2024).

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

    [40] TAO Shijie, ZHANG Lifei, ZHANG Qingyi et al. Improved naive Bayesian probability classifier in nuclear charge radius prediction[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 52, 83-95(2022).

    [41] Wu X H, Zhao P W. Predicting nuclear masses with the kernel ridge regression[J]. Physical Review C, 101, 051301(2020).

    [42] Wu X H, Guo L H, Zhao P W. Nuclear masses in extended kernel ridge regression with odd-even effects[J]. Physics Letters B, 819, 136387(2021).

    [43] Wu X H, Pan C, Zhang K Y et al. Nuclear mass predictions of the relativistic continuum Hartree-Bogoliubov theory with the kernel ridge regression[J]. Physical Review C, 109, 024310(2024).

    [44] Wang N, Liu M. Nuclear mass predictions with a radial basis function approach[J]. Physical Review C, 84, 051303(2011).

    [45] Niu Z M, Zhu Z L, Niu Y F et al. Radial basis function approach in nuclear mass predictions[J]. Physical Review C, 88, 024325(2013).

    [46] Zheng J S, Wang N Y, Wang Z Y et al. Mass predictions of the relativistic mean-field model with the radial basis function approach[J]. Physical Review C, 90, 014303(2014).

    [47] Niu Z M, Sun B H, Liang H Z et al. Improved radial basis function approach with odd-even corrections[J]. Physical Review C, 94, 054315(2016).

    [48] Niu Z M, Liang H Z, Sun B H et al. High precision nuclear mass predictions towards a hundred kilo-electron-volt accuracy[J]. Science Bulletin, 63, 759-764(2018).

    [49] Utama R, Piekarewicz J, Prosper H B. Nuclear mass predictions for the crustal composition of neutron stars: a Bayesian neural network approach[J]. Physical Review C, 93, 014311(2016).

    [50] Utama R, Piekarewicz J. Refining mass formulas for astrophysical applications: a Bayesian neural network approach[J]. Physical Review C, 96, 044308(2017).

    [51] Niu Z M, Liang H Z. Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects[J]. Physics Letters B, 778, 48-53(2018).

    [52] Léo N, Cao Y C, Witold N et al. Bayesian approach to model-based extrapolation of nuclear observables[J]. Physical Review C, 98, 034318(2018).

    [53] LI Jiaxing, ZHAO Tianliang, MA Nana et al. Application of neural network approach in nuclear mass[J]. Atomic Energy Science and Technology, 57, 696-703(2023).

    [54] LOU Yueshen, GUO Wenjun. Prediction of unknown nuclear stability by Bayesian deep neural network[J]. Acta Physica Sinica, 71, 163-172(2022).

    [55] Xie J Z, Wang K P, Wang C et al. Novel Bayesian probability method in predictions of nuclear masses[J]. Physical Review C, 109, 064317(2024).

    [56] Shelley M, Pastore A. A new mass model for nuclear astrophysics: crossing 200 keV accuracy[J]. Universe, 7, 131(2021).

    [57] Gao Z P, Wang Y J, Lyu H L et al. Machine learning the nuclear mass[J]. Nuclear Science and Techniques, 32, 109(2021).

    [58] Wu X H, Lu Y Y, Zhao P W. Multi-task learning on nuclear masses and separation energies with the kernel ridge regression[J]. Physics Letters B, 834, 137394(2022).

    [59] Wu X H, Pan C. Nuclear mass predictions with anisotropic kernel ridge regression[J]. Physical Review C, 110, 034322(2024).

    [60] Du X K, Guo P, Wu X H et al. Examination of machine learning for assessing physical effects: Learning the relativistic continuum mass table with kernel ridge regression[J]. Chinese Physics C, 47, 074108(2023).

    [61] Guo L H, Wu X H, Zhao P W. Nuclear mass predictions of the relativistic density functional theory with the kernel ridge regression and the application to r-process simulations[J]. Symmetry, 14, 1078(2022).

    [62] Wu X H, Ren Z X, Zhao P W. Nuclear energy density functionals from machine learning[J]. Physical Review C, 105, L031303(2022).

    [63] Chen Y Y, Wu X H. Machine learning nuclear orbital-free density functional based on Thomas-Fermi approach[J]. International Journal of Modern Physics E, 33, 2450012(2024).

    [64] Ma J Q, Zhang Z H. Improved phenomenological nuclear charge radius formulae with kernel ridge regression[J]. Chinese Physics C, 46, 074105(2022).

    [65] Tang L, Zhang Z H. Nuclear charge radius predictions by kernel ridge regression with odd-even effects[J]. Nuclear Science and Techniques, 35, 19(2024).

    [66] Huang T X, Wu X H, Zhao P W. Application of kernel ridge regression in predicting neutron-capture reaction cross-sections[J]. Communications in Theoretical Physics, 74, 095302(2022).

    Tools

    Get Citation

    Copy Citation Text

    Yueying GUO, Xiangqi TANG, Huixin LIU, Xinhui WU. Nucleon separation energy predictions with the Kernel ridge regression[J]. NUCLEAR TECHNIQUES, 2025, 48(5): 050003

    Download Citation

    EndNote(RIS)BibTexPlain 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. 6, 2025

    Accepted: --

    Published Online: Jun. 26, 2025

    The Author Email: Xinhui WU (吴鑫辉)

    DOI:10.11889/j.0253-3219.2025.hjs.48.250096

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology