论文标题
学习不确定性的频繁方式：高能量物理学中的校准和相关性
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
论文作者
论文摘要
校准是一个常见的实验物理问题，其目的是在给定数量x给定量x的情况下推断出不可观察数量的值和不确定性。此外，人们希望量化x和z相关的程度。在本文中，我们提出了一个机器学习框架，用于使用高斯不确定性估计进行频繁的最大似然推理，该框架还量化了不可观察和测量数量之间的相互信息。该框架使用Kullback-Leibler Divergence的Donsker-Varadhan表示（用新型的高斯ANSATZ参数化），以同时提取单个训练中的最大似然值，不确定性和共同信息的同时提取。我们通过从大型强子对撞机的CMS检测器的模拟中提取喷气能校正和分辨率来证明我们的框架。通过利用喷气机内部的高维特征空间，我们将标称CMS喷射的分辨率提高了15％以上。
Calibration is a common experimental physics problem, whose goal is to infer the value and uncertainty of an unobservable quantity Z given a measured quantity X. Additionally, one would like to quantify the extent to which X and Z are correlated. In this paper, we present a machine learning framework for performing frequentist maximum likelihood inference with Gaussian uncertainty estimation, which also quantifies the mutual information between the unobservable and measured quantities. This framework uses the Donsker-Varadhan representation of the Kullback-Leibler divergence -- parametrized with a novel Gaussian Ansatz -- to enable a simultaneous extraction of the maximum likelihood values, uncertainties, and mutual information in a single training. We demonstrate our framework by extracting jet energy corrections and resolution factors from a simulation of the CMS detector at the Large Hadron Collider. By leveraging the high-dimensional feature space inside jets, we improve upon the nominal CMS jet resolution by upwards of 15%.
