Theory and simulations of covariance mapping in multiple dimensions for data analysis in high-event-rate experiments
Theory and simulations of covariance mapping in multiple dimensions for data analysis in high-event-rate experiments
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用于高事件率实验中数据分析的多维协方差映射的理论和模拟
DOI：
10.1103/physreva.89.053418
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发表时间：
2014
影响因子：
2.9
通讯作者：
Zhaunerchyk V
中科院分区：
文献类型：
--
作者：
Zhaunerchyk V
从理论上研究了多维协方差分析及其对多产品过程相关性的有效性。需要纠正假相关引起的实验参数波动，从拍摄到拍摄，如自放大自发辐射x射线自由电子激光脉冲的强度，强调。三倍协方差分析的基础上简单的扩展的两个变量的制定是有效的变量表现出泊松统计。在这种情况下，由与信号线性缩放的不稳定实验参数的波动引起的假相关性可以通过三重偏协方差分析来消除，如这里所定义的。基于相同的简单扩展的四倍协方差被发现是无效的一般。当不稳定参数的波动引起非线性信号变化时，本文提出了一种抑制假相关的权变协方差分析技术。在本文中，我们还展示了一种方法，以消除虚假的相关性与波动的几个不稳定的实验参数。
Multidimensional covariance analysis and its validity for correlation of processes leading to multiple products are investigated from a theoretical point of view. The need to correct for false correlations induced by experimental parameters which fluctuate from shot to shot, such as the intensity of self-amplified spontaneous emission x-ray free-electron laser pulses, is emphasized. Threefold covariance analysis based on simple extension of the two-variable formulation is shown to be valid for variables exhibiting Poisson statistics. In this case, false correlations arising from fluctuations in an unstable experimental parameter that scale linearly with signals can be eliminated by threefold partial covariance analysis, as defined here. Fourfold covariance based on the same simple extension is found to be invalid in general. Where fluctuations in an unstable parameter induce nonlinear signal variations, a technique of contingent covariance analysis is proposed here to suppress false correlations. In this paper we also show a method to eliminate false correlations associated with fluctuations of several unstable experimental parameters.