论文标题
在未来的$ e^-e^+$ calliders与机器上学习物理学
Learning Physics at Future $e^-e^+$ Colliders with Machine
论文作者
论文摘要
喷射聚类中的信息变形和损失是确切测量$ e^-e^-e^+$ colliders hadronic事件的主要局限性之一。由于它们在数据中的主导地位，此类事件的测量对于在未来几十年中推进了希格斯和电动物理学的精确边界至关重要。我们表明，通过将事件级信息协同为数据分析，可以通过深度中性网络的技术协同效应，可以通过将事件级别的信息协同良好。关于这一点，我们引入了类似CMB的可观察方案，其中事件级运动学被编码为前命令和多谱处的Fox-Wolfram（FW）矩，以更高的订单编码。然后，我们开发了一系列的喷气级（W/和W/ o FW矩）和事件级别的分类器，并与两射流和四射流事件相比分析其灵敏度性能。作为一个应用程序，我们分析以5ab $^{ - 1}@$ 240GEV的数据为$ e^-e^+$ colliders测量higgs衰减宽度。获得的精度明显优于文档中提出的基线。我们希望该策略将在未来的$ e^-e^+$ colliders上应用于许多其他Hadronic事件测量值，并打开一个新的角度来评估其物理能力。
Information deformation and loss in jet clustering are one of the major limitations for precisely measuring hadronic events at future $e^-e^+$ colliders. Because of their dominance in data, the measurements of such events are crucial for advancing the precision frontier of Higgs and electroweak physics in the next decades. We show that this difficulty can be well-addressed by synergizing the event-level information into the data analysis, with the techniques of deep neutral network. In relation to this, we introduce a CMB-like observable scheme, where the event-level kinematics is encoded as Fox-Wolfram (FW) moments at leading order and multi-spectra at higher orders. Then we develop a series of jet-level (w/ and w/o the FW moments) and event-level classifiers, and analyze their sensitivity performance comparatively with two-jet and four-jet events. As an application, we analyze measuring Higgs decay width at $e^-e^+$ colliders with the data of 5ab$^{-1}@$240GeV. The precision obtained is significantly better than the baseline ones presented in documents. We expect this strategy to be applied to many other hadronic-event measurements at future $e^-e^+$ colliders, and to open a new angle for evaluating their physics capability.
