Unfolding is an important procedure in particle physics experiments which corrects for detector effects and provides differential cross section measurements that can be used for a number of downstream tasks, such as extracting fundamental physics parameters. Traditionally, unfolding is done by discretizing the target phase space into a finite number of bins and is limited in the number of unfolded variables. Recently, there have been a number of proposals to perform unbinned unfolding with machine learning. However, none of these methods (like most unfolding methods) allow for simultaneously constraining (profiling) nuisance parameters. We propose a new machine learning-based unfolding method that results in an unbinned differential cross section and can profile nuisance parameters. The machine learning loss function is the full likelihood function, based on binned inputs at detector-level. We first demonstrate the method with simple Gaussian examples and then show the impact on a simulated Higgs boson cross section measurement.

翻译：去卷积是粒子物理实验中的重要步骤，用于修正探测器效应并提供微分截面测量结果，这些结果可用于多项下游任务（如提取基本物理参数）。传统去卷积方法需将目标相空间离散化为有限数量箱格，且可去卷积的变量数量受到限制。近期虽有多项基于机器学习的无箱化去卷积方案提出，但此类方法（如同大多数去卷积方法）均无法同时约束（轮廓化）干扰参数。我们提出一种基于机器学习的新型去卷积方法，既能生成无箱化微分截面，又可对干扰参数进行轮廓化处理。该方法的机器学习损失函数是基于探测器层有箱输入的完整似然函数。我们首先通过简单高斯示例验证该方法，继而展示其在模拟希格斯玻色子截面测量中的实际效果。