电磁量能器数据的运动学拟合——一种改进方法
Kinematic Fitting of electromagnetic calorimeter data - an improved method
- Helmholtz-Institut für Strahlen- und Kernphysik and Cluster of Excellence ”Color meets Flavor”, Universität Bonn, Germany（波恩大学斯特拉斯尔与核物理亥姆霍兹研究所及“颜色遇见味道”卓越集群）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对电磁量能器能量测量非高斯分布导致运动学拟合不准的问题，提出将非高斯变量变换为高斯变量的改进方法，提高了拟合精度、稳健性及信号本底比。
AI中文摘要：
运动学拟合在粒子物理实验中广泛使用，是一种强大的工具，用于提高实验分辨率、抑制本底以及为特定反应的识别提供选择判据。然而，运动学拟合方法通常假设被拟合的量遵循高斯分布，这一假设并非总是成立，对于电磁量能器的能量测量尤其如此。这一问题可以通过将非高斯变量变换为遵循高斯分布的变量来在很大程度上得到克服。新的自适应运动学拟合程序能够更好地考虑所测量的量能器能量的非高斯性质，同时提高运动学拟合的准确性和稳健性。这导致运动学拟合的拉量和置信水平分布得到改善，最终事件样本中的不变量质量分布和信号与本底之比也得到改善。
英文摘要：
Kinematic fitting is widely used in particle physics experiments as a powerful tool to improve experimental resolutions, to suppress background and to provide selection criteria for the identification of specific reactions. Kinematic fitting methods, however, typically assume that the fitted quantities follow Gaussian distributions; an assumption which does not always hold. This is particularly true for energy measurements from electromagnetic calorimeters. This issue can largely be overcome by performing a transformation of the non-Gaussian variables into variables which follow a Gaussian distribution. The new adapted kinematic fitting procedure allows to better account for the non-Gaussian nature of the measured calorimeter energies, at the same time improving the accuracy and robustness of the kinematic fit. This leads to improved pull and confidence-level distributions of the kinematic fit as well as improved invariant mass distributions and signal-to-background ratios in the final event sample.
