利用机器学习技术校准CMS量热器中电磁簇射特征
Calibration of electromagnetic shower features in the CMS calorimeter with machine-learning techniques
AI总结：
本文提出基于重加权和归一化流的两种机器学习校准方法，用于修正CMS量热器电磁簇射模拟特征，以改善与2022年13.6 TeV质子碰撞数据的一致性。
AI中文摘要：
蒙特卡洛模拟在高能粒子物理分析中被广泛使用。然而，对底层物理的不完整描述可能导致模拟数据与碰撞数据中观测量之间的显著差异。为减轻此类错误建模可能带来的偏差，对模拟数据进行校准以匹配真实数据至关重要。本文提出了两种基于机器学习技术的新型校准方法：一种为重加权方法，利用分类器学习模拟数据与真实数据之间概率密度函数的比率；另一种为归一化流方法，学习一个高维变换以将模拟数据映射到真实数据。与传统校准方法相比，这两种方法均能在高维特征空间中提供连续、无分箱的修正，从而改善与数据的整体一致性。这些技术以校正CMS量热器中模拟电磁簇射的特征为例进行了演示，使用了2022年采集的质心能量为$\sqrt{s}$ = 13.6 TeV的质子-质子碰撞数据，对应积分亮度为26.7 fb$^{-1}$。文中比较了这两种方法的优势与局限性。
英文摘要：
Monte Carlo simulations are used extensively in high-energy particle physics analyses. However, an incomplete description of the underlying physics can lead to significant discrepancies between observables measured in simulated and collision data. To mitigate potential biases arising from such mismodelling, it is essential to calibrate simulations to data. This paper presents two novel calibration methods based on machine-learning techniques: a reweighting approach, which employs a classifier to learn the ratio of probability density functions between simulation and data; and a normalising-flow approach, which learns a high-dimensional transformation to map simulation to data. Compared to traditional calibration methods, both approaches offer continuous, unbinned corrections across high-dimensional feature spaces, enabling improved global agreement with data. The techniques are demonstrated in the context of correcting the features of simulated electromagnetic showers in the CMS calorimeters, using proton-proton collision data collected during 2022 at $\sqrt{s}$ = 13.6 TeV, corresponding to an integrated luminosity of 26.7 fb$^{-1}$. The strengths and limitations of the two methods are compared.
