arXiv 2004.150132020-04hep-phhep-ex

在未来的$e^-e^+$对撞机上用机器学习物理

Learning Physics at Future $e^-e^+$ Colliders with Machine

Lingfeng Li, Ying-Ying Li, Tao Liu, Si-Jun Xu

备注：: 39 pages, 21 figures. Details clarified, typos fixed, references added
DOI：: 10.1007/JHEP10(2020)018

摘要：

喷流成团过程中的信息变形和损失是未来$e^-e^+$对撞机精确测量强子事件的主要限制之一。由于它们在数据中的主导地位，对这些事件的测量对于在未来几十年内推进希格斯粒子和电弱物理学的精确前沿至关重要。我们表明，通过将事件级信息与深度神经网络技术协同作用到数据分析中，可以很好地解决这一困难。与此相关，我们引入了一个类似于CMB的可观测方案，其中事件级运动学被编码为Fox-Wolfram（FW）矩在领先的顺序和多光谱在更高的订单。然后，我们开发了一系列的喷流级（w/和w/o FW矩）和事件级分类器，并分析其灵敏度性能与两个喷流和四喷流事件进行比较。作为应用，我们分析了在$e^-e^+$对撞机上用5ab $^{-1}@$ 240 GeV的数据测量Higgs衰变宽度的情况。所获得的精度明显优于文献中提出的基线精度。我们期望这一策略能够应用于未来$e^-e^+$对撞机上的其他强子事件测量，并为评估其物理能力开辟一个新的角度。

英文摘要：

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.

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。

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