矩展开
Moment Unfolding
AI总结：
提出矩展开技术，利用机器学习直接展开分布矩，无需分箱，在对撞机喷注子结构测量中比基于分箱的方法更精确，精度与完全不分箱方法相当或更高。
摘要：
在粒子物理和核物理中，将探测器畸变进行去卷积（“展开”）是将截面测量与理论预测进行比较的关键步骤。然而，大多数现有方法需要直方图分箱，而许多理论预测是基于统计矩的。我们开发了一种新方法，可以直接将分布矩作为另一个可观测量的函数进行展开，而无需先将数据离散化。我们的矩展开技术使用机器学习，并受到生成对抗网络（GANs）的启发。我们利用对撞机物理中的喷注子结构测量来展示该方法的性能。通过这个示例，我们发现我们的矩展开协议比基于分箱的方法更精确，并且与完全不分箱的方法相比，精度相当或更高。
英文摘要：
Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our Moment Unfolding technique uses machine learning and is inspired by Generative Adversarial Networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our Moment Unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
