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Weizsäcker-Skyrme核质量模型的统计误差研究

Statistical Errors in Weizsäcker-Skyrme Mass Model

  • 摘要: 基于最大值近似估算的方法,系统地研究了Weizsäcker-Skyrme(WS4)核质量模型的参数不确定性,并计算了WS4核质量模型理论预言值的统计误差。WS4核质量模型的理论预言值与实验值的偏差基本都小于模型的统计误差,表明采用最大值近似估算法对WS4核质量模型理论预言的统计误差的分析是简捷而有效的。进一步研究了WS4核质量模型理论计算中最敏感的参数,结果表明,对称能系数相关的两个参数csymκ对中子滴线附近的原子核质量有重要影响。此外还对WS4模型与WS*模型的参数不确定性及统计误差进行了对比研究,WS4模型中各模型参数的不确定性比WS*模型中相应模型参数的不确定性降低了10%~ 50%。


    The statistical uncertainties of 15 model parameters in the Weizsäcker-Skyrme(WS4) mass model are investigated with an efficient approach, and the propagated errors in the predicted masses are estimated. The discrepancies between the predicted masses and the experimental data are almost all smaller than the model errors. The most sensitive model parameter which causes the largest statistical error is analyzed for all bound nuclei. We find that the two coefficients of symmetry energy term significantly influence the mass predictions of extremely neutron-rich nuclei. In addition, the parameter uncertainties and statistical errors of the WS4 mass model and the WS* mass model are compared. The uncertainties of model parameter in the WS4 mass model is reduced by 10% ~ 50% compared with the WS* mass model.

     

    Abstract: The statistical uncertainties of 15 model parameters in the Weizsäcker-Skyrme(WS4) mass model are investigated with an efficient approach, and the propagated errors in the predicted masses are estimated. The discrepancies between the predicted masses and the experimental data are almost all smaller than the model errors. The most sensitive model parameter which causes the largest statistical error is analyzed for all bound nuclei. We find that the two coefficients of symmetry energy term significantly influence the mass predictions of extremely neutron-rich nuclei. In addition, the parameter uncertainties and statistical errors of the WS4 mass model and the WS* mass model are compared. The uncertainties of model parameter in the WS4 mass model is reduced by 10% ~ 50% compared with the WS* mass model.

     

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