论文标题
基于高斯工艺的计算在哪里试验因素
Gaussian Process-based calculation of look-elsewhere trials factor
论文作者
论文摘要
在高能物理学中，有效，精确（足够）计算例如潜在的新共鸣的全球意义是一个反复出现的挑战。我们提出了一种新方法，将搜索区域的重要性建模为高斯过程。高斯过程的内核使用协方差矩阵近似，并使用经过精心设计的仅背景数据集进行计算，与典型分析中使用的仅随机背景数据集相当，该数据集依赖于显着性的平均上流。低和中等显着性的试验因子随后可以通过计算廉价的高斯过程随机抽样计算为所需的精度。此外，一旦确定了高斯过程的协方差，就可以通过分析计算上的上流数量。在我们的工作中，我们给出了分析计算的一些亮点，还讨论了有限网格的试验因素估计的一些特殊性。我们通过研究三个互补统计模型的研究来说明方法。
In high-energy physics it is a recurring challenge to efficiently and precisely (enough) calculate the global significance of, e.g., a potential new resonance. We propose a new method that models the significance in the search region as a Gaussian Process. The kernel of the Gaussian Process is approximated with a covariance matrix and is calculated with a carefully designed set of background-only data sets, comparable in number to the random background-only data sets used in a typical analysis that relies on the average upcrossings of the significance. The trials factor for both low and moderate significances can subsequently be calculated to the desired precision with a computationally inexpensive random sampling of the Gaussian Process. In addition, once the covariance of the Gaussian Process is determined, the average number of upcrossings can be computed analytically. In our work we give some highlights of the analytic calculation and also discuss some peculiarities of the trials factor estimation on a finite grid. We illustrate the method with studies of three complementary statistical models.
