标题： 将仿真转换为无需配对的数据 显示英文标题

标题： Transforming Simulation to Data Without Pairing

摘要： 我们探索了一种基于生成式机器学习的方法，用于使用统计独立但相关的控制样本来估计目标样本中的多维概率密度函数（PDF）——这是粒子物理数据分析中的一个常见挑战。生成模型必须根据来自控制样本的输入多维分布准确地再现各个可观测量的分布，并保持它们之间的相关性。在这里，我们提出了一种基于链式双射器的条件归一化流模型（CNF），该模型学会将未配对的模拟事件转换为数据事件。我们在大型强子对撞机（LHC）希格斯到双光子分析的背景下评估了CNF模型的表现，其中我们使用CNF模型将蒙特卡洛双光子样本转换为模拟数据样本。我们展示了CNF模型能够准确地模拟复杂的数据分布和相关性。我们还利用最近流行的改进微分乘数法（MDMM）来提高模型的收敛性，并赋予通常任意的损失函数参数以物理意义。 显示英文摘要

摘要： We explore a generative machine learning-based approach for estimating multi-dimensional probability density functions (PDFs) in a target sample using a statistically independent but related control sample - a common challenge in particle physics data analysis. The generative model must accurately reproduce individual observable distributions while preserving the correlations between them, based on the input multidimensional distribution from the control sample. Here we present a conditional normalizing flow model (CNF) based on a chain of bijectors which learns to transform unpaired simulation events to data events. We assess the performance of the CNF model in the context of LHC Higgs to diphoton analysis, where we use the CNF model to convert a Monte Carlo diphoton sample to one that models data. We show that the CNF model can accurately model complex data distributions and correlations. We also leverage the recently popularized Modified Differential Multiplier Method (MDMM) to improve the convergence of our model and assign physical meaning to usually arbitrary loss-function parameters.