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
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    Figures & Tables(5)
    The number of nuclei with the corresponding separation energy deviations from the data locating in various slots
    The deviations of one-neutron separation energy Sn in the WS4, KRR, and KRRoe predictions, as well as the experimental data, from the WS4 results for Ruthenium Ru (a) and Plumbum Pb (b) isotopes
    Comparison of the extrapolation power of the KRR and the KRRoe approach for eight test sets with different extrapolation distances (a) Two-neutron (S2n) separation energies, (b) Two-proton (S2p) separation energies (color online)
    Comparison of the extrapolation power of the KRR and the KRRoe approach for eight test sets with different extrapolation distances (a) One-neutron (Sn) separation energies, (b) One-proton (Sp) separation energies (color online)
    • Table 1. Root-Mean-Square (RMS) deviations of nuclear mass and separation energies between experimental data and WS4, KRR and KRRoe predictions

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      Table 1. Root-Mean-Square (RMS) deviations of nuclear mass and separation energies between experimental data and WS4, KRR and KRRoe predictions

      方均根偏差RMS of deviations / keV
      eeeooeooTotal
      MWS4281285277299285
      KRR203183174215194
      KRRoe97115119149121
      S2nWS4258256257273261
      KRR196188180221197
      KRRoe134172153223173
      S2pWS4309301284319304
      KRR219202190233211
      KRRoe146156193237185
      SnWS4260237271250255
      KRR269262284274272
      KRRoe160161218226193
      SpWS4258269248280264
      KRR250269255286265
      KRRoe171214156220192
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    Yueying GUO, Xiangqi TANG, Huixin LIU, Xinhui WU. Nucleon separation energy predictions with the Kernel ridge regression[J]. NUCLEAR TECHNIQUES, 2025, 48(5): 050003

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    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

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