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arXiv 1302.5745physics.plasm-phphysics.data-anstat.AP
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NIF上惯性约束聚变实验分析用贝叶斯方法的发展

Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF

Jim A Gaffney, Dan Clark, Vijay Sonnad, Stephen B Libby

摘要:

惯性约束聚变(ICF)实验的复杂性导致了非常大量的实验参数,这些参数仅以有限的可靠性已知。这些参数,加上无数的物理模型,管理目标的演变,使可靠的提取物理实验活动非常困难。我们开发了一种推理方法,允许所有重要的实验参数,和以前的知识,要考虑到在调查底层的微观物理模型。其结果是作为一个修改后的$χ^{2}$分析,这是很容易实现在现有的分析,相当便携。我们提出了第一个应用程序,最近收敛烧蚀实验在NIF进行,并调查在所有物理尺寸的目标(很难做到使用其他方法)的变化的影响。我们发现,对于特征明确的目标,其中尺寸在0.5%的水平上变化,几乎没有影响,但3%的变化会显着改变推断的结果。我们的贝叶斯方法允许特定的推断结果与微观物理模型中的先验误差相关联;在我们的示例中,调整碳不透明度以匹配实验数据(即,忽略先验知识)等效于Tabop不透明度表中400%的假定先验误差。这个大误差是不合理的,强调了在分析这些实验时包括先验知识的重要性。

英文摘要:

The complex nature of inertial confinement fusion (ICF) experiments results in a very large number of experimental parameters that are only known with limited reliability. These parameters, combined with the myriad physical models that govern target evolution, make the reliable extraction of physics from experimental campaigns very difficult. We develop an inference method that allows all important experimental parameters, and previous knowledge, to be taken into account when investigating underlying microphysics models. The result is framed as a modified $χ^{2}$ analysis which is easy to implement in existing analyses, and quite portable. We present a first application to a recent convergent ablator experiment performed at the NIF, and investigate the effect of variations in all physical dimensions of the target (very difficult to do using other methods). We show that for well characterised targets in which dimensions vary at the 0.5% level there is little effect, but 3% variations change the results of inferences dramatically. Our Bayesian method allows particular inference results to be associated with prior errors in microphysics models; in our example, tuning the carbon opacity to match experimental data (i.e., ignoring prior knowledge) is equivalent to an assumed prior error of 400% in the tabop opacity tables. This large error is unreasonable, underlining the importance of including prior knowledge in the analysis of these experiments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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