AI 中文总结
本研究将人工智能技术应用于ATLAS实验，首次在LHC完成Z+喷注产生截面的全相空间微分测量，为该过程提供完整实验表征，为后续同类测量提供原理验证。
AI 中文摘要
大型强子对撞机（LHC）上的质子-质子碰撞为观测极高能标下基本粒子的相互作用提供了机会。高碰撞率以及每次碰撞产生的大量末态粒子意味着ATLAS实验等探测器生成的数据集规模庞大且维度很高。这些数据集的复杂性要求使用新颖的数据分析技术，以充分利用所有可用信息，阐明已知的相互作用并寻找新的相互作用。本论文展示了人工智能（AI）技术在提升ATLAS实验物理结果方面的应用，核心是对LHC上Z+喷注产生截面进行全相空间测量，该测量通过基于AI的展开算法，对每个末态带电粒子的运动学量进行微分截面测量。这是LHC上首次开展此类测量，它为Z+喷注产生过程提供了完整的实验表征，并为在LHC众多过程中进一步开展此类测量提供了原理验证。
英文摘要
Proton-proton collisions at the Large Hadron Collider (LHC) offer the opportunity to observe the interactions of fundamental particles at very high energy scales. The high collision rate and large number of final state particles produced per collision imply that the datasets produced by detectors such as the ATLAS experiment are large and high dimensional. The complexity of these datasets calls for the use of novel data analysis techniques which can exploit all of the available information to illuminate known interactions and search for new ones. This thesis presents applications of artificial intelligence (AI) techniques to improve the physics results of the ATLAS experiment. It centers on a full-phase-space measurement of the Z+jets production cross section at the LHC, where the cross section is measured differential in the kinematics of every final state charged particle through the use of an AI-based unfolding algorithm. This is the first such measurement performed at the LHC, which provides a complete experimental characterization of the Z+jets production process and a proof-of-principle for the further pursuit of such measurements on a host of LHC processes.
CommentsPh.D. Thesis, 313 pages
