Which Nuclei are Well Described by Liquid Drop Model: A Statistical Study Based on Uncertainty Decomposition Method
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摘要: 一个模型适合描述哪些物理量? 这个问题可以通过模型的物理来源来回答。比如，液滴模型适合描述重核和远离满壳核。这是因为液滴近似更适用于核子数多的核以及液滴模型不包含壳效应。这样的回答是定性的并需要清楚模型的物理来源。是否可能仅通过模型的数学形式和实验数据就能给出半定量的解答? 利用最近提出的不确定度分解方法尝试对液滴模型适合描述哪些核这一问题进行半定量的回答。并且不需已知液滴模型的物理来源，仅需其数学形式以及实验数据。通过不确定度分解方法，液滴模型与实验数据间的残差可以分解为系统不确定度和统计不确定度。两者分别代表了模型的缺陷和模型不精确的参数带来的不确定度。基于这一分解，核素图上的原子核可以按其对应的残差被半定量地划分为系统不确定度主导、统计不确定度主导、以及中间区域。液滴模型适合描述的核就是统计不确定度主导残差的核而不是像通常认为的是残差最小的核。从核素图上看，统计不确定度主导残差的核正是重核以及远离满壳核，与液滴模型物理来源一致，但得到这一结果的过程是半定量的且仅需液滴模型的数学形式以及实验数据。如果对由统计不确定度主导残差的核重新拟合液滴模型的参数，模型可以很好地描述这些核（标准差小于0.7 MeV）。
Which data are well described by a theoretical model? Such questions can be answered through the physical origin of the model. For example, the liquid drop model (LDM) well describes the heavy and far from shell nuclei. Because the liquid-drop assumption is more suitable for nuclei with more nucleons and LDM does not include the shell effect. Such answer is qualitative and needs a clear view on the physical origin of the model. Is it possible to give an semi-quantitatively answer only from the mathematical form of the model and the observed data. In the present work, the recently suggested uncertainty decomposition method (UDM) is used to answer which nuclei are well described by LDM. The residues between LDM and the observed data can be decomposed through UDM to systematic and statistical uncertainties, which represent the uncertainty of the deficiency of the model and the indeterminate parameters, respectively. Based on UDM, the chart of nuclides are semi-quantitatively divided into three parts, areas dominated by the systematic and statistical uncertainties, and the cross area. Contrary to the common sense, the well described nuclei by LDM are not the nuclei with small residues, but actually the nuclei of which the residues are dominated by the statistical uncertainty. These nuclei are indeed the heavy and far from shell nuclei, which agrees with the physical consideration of LDM. But only the mathematical form of the model and the experimental data are needed during the use of UDM. The nuclides dominated by the statistical uncertainty can be well described by LDM (standard deviation less than 0.7 MeV) with parameters fitting to these nuclei.Abstract: Which data are well described by a theoretical model? Such questions can be answered through the physical origin of the model. For example, the liquid drop model (LDM) well describes the heavy and far from shell nuclei. Because the liquid-drop assumption is more suitable for nuclei with more nucleons and LDM does not include the shell effect. Such answer is qualitative and needs a clear view on the physical origin of the model. Is it possible to give an semi-quantitatively answer only from the mathematical form of the model and the observed data. In the present work, the recently suggested uncertainty decomposition method (UDM) is used to answer which nuclei are well described by LDM. The residues between LDM and the observed data can be decomposed through UDM to systematic and statistical uncertainties, which represent the uncertainty of the deficiency of the model and the indeterminate parameters, respectively. Based on UDM, the chart of nuclides are semi-quantitatively divided into three parts, areas dominated by the systematic and statistical uncertainties, and the cross area. Contrary to the common sense, the well described nuclei by LDM are not the nuclei with small residues, but actually the nuclei of which the residues are dominated by the statistical uncertainty. These nuclei are indeed the heavy and far from shell nuclei, which agrees with the physical consideration of LDM. But only the mathematical form of the model and the experimental data are needed during the use of UDM. The nuclides dominated by the statistical uncertainty can be well described by LDM (standard deviation less than 0.7 MeV) with parameters fitting to these nuclei. -
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