论文标题
通过机器学习和力矩分解来增强搜索共振
Enhancing searches for resonances with machine learning and moment decomposition
论文作者
论文摘要
搜索共鸣的新物理学的一个主要挑战是，受过培训以增强潜在信号的分类器不得诱导局部结构。当使用边带方法从数据估算背景时，此类结构可能会导致错误信号。已经开发了各种技术来构建与共振特征（通常是质量）独立的分类器。这样的策略足以避免局部结构，但不是必需的。我们使用新颖的力矩损耗函数（时刻分解或模式）开发了一组新的工具，该工具放宽了独立性的假设而无需在后台创建结构。通过允许分类器更加灵活，我们可以增强对新物理学的敏感性，而不会损害背景估计的保真度。
A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures. Such structures could result in a false signal when the background is estimated from data using sideband methods. A variety of techniques have been developed to construct classifiers which are independent from the resonant feature (often a mass). Such strategies are sufficient to avoid localized structures, but are not necessary. We develop a new set of tools using a novel moment loss function (Moment Decomposition or MoDe) which relax the assumption of independence without creating structures in the background. By allowing classifiers to be more flexible, we enhance the sensitivity to new physics without compromising the fidelity of the background estimation.
