In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements. This task has been extensively studied in particle physics, where the deconvolution task is called unfolding. A number of recent methods have shown how to perform high-dimensional, unbinned unfolding using machine learning. However, one of the assumptions in all of these methods is that the detector response is correctly modeled in the Monte Carlo simulation. In practice, the detector response depends on a number of nuisance parameters that can be constrained with data. We propose a new algorithm called Profile OmniFold, which works in a similar iterative manner as the OmniFold algorithm while being able to simultaneously profile the nuisance parameters. We illustrate the method with a Gaussian example as a proof of concept highlighting its promising capabilities.

翻译：在许多实验情境中，为对测量结果进行物理解释，需要从统计上消除仪器效应的影响。这一任务在粒子物理学中已得到广泛研究，其中解卷积任务被称为“反卷积”。近期多项研究展示了如何利用机器学习进行高维、无分箱的反卷积。然而，这些方法均基于一个假设：蒙特卡洛模拟中探测器响应的建模是准确的。实际上，探测器响应依赖于若干可通过数据约束的干扰参数。本文提出一种名为Profile OmniFold的新算法，该算法以与OmniFold算法类似的迭代方式工作，同时能够对干扰参数进行同步剖面分析。我们通过高斯分布示例演示该方法，作为概念验证以突显其潜在能力。