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Bayesian inference of deceleration-phase Rayleigh-Taylor growth rates in laser-driven cylindrical implosions
High Energy Density Physics ( IF 1.6 ) Pub Date : 2020-09-19 , DOI: 10.1016/j.hedp.2020.100879
B. Tobias , C.F. Kawaguchi , S. Palaniyappan , J.P. Sauppe , K.A. Flippo , J.L. Kline

An iterative forward modeling approach has been developed and applied to the analysis of radiography data from cylindrical Rayleigh-Taylor instability studies performed at the Omega laser facility. Synthetic radiographs are generated and iterated using the Bayes’ Inference Engine [1] to produce maximum likelihood estimates for the time-dependent amplitude of deceleration-phase Rayleigh-Taylor modes seeded by a sinusoidal perturbation in the aluminum marker layer. This iterative forward modeling approach self-consistently fits the magnification and parallax in the image, both of which are sensitive at this scale (~13x magnification) to misalignments of the pinhole smaller than 200 μm. Systematic errors in the inference of growth factor as large as 10% have been identified and eliminated by this technique. Furthermore, the self-consistent modeling of image parallax reveals that these implosions are not elliptical as they may appear to the casual observer, but indeed highly symmetric. The regularizing priors adopted here, and further constraints that might be applied in future work, reduce experimental uncertainties and lend greater statistical significance to comparisons with hydrodynamic modeling.



中文翻译:

激光驱动圆柱内爆中减速相Rayleigh-Taylor增长率的贝叶斯推断

已经开发出一种迭代的正向建模方法,并将其应用于在Omega激光设备上进行的圆柱状Rayleigh-Taylor不稳定性研究中的射线照相数据分析。使用贝叶斯推理引擎[1]生成并迭代合成射线照片,以产生最大似然估计,以估算铝标记层中正弦波扰动所引起的减速相位瑞利-泰勒模式随时间变化的幅度。这种迭代的正向建模方法始终一致地适合图像中的放大倍率和视差,这两者在此比例下(〜13倍放大倍数)都对小于200μm的针孔未对准敏感。通过这种技术,可以识别并消除高达10%的生长因子推断中的系统误差。此外,图像视差的自洽建模显示,这些内爆不是像散乱的观察者所看到的那样呈椭圆形,而是高度对称的。此处采用的正则化先验条件以及可能在未来工作中应用的其他约束条件,减少了实验不确定性,并为与水动力模型的比较提供了更大的统计意义。

更新日期:2020-09-23
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