基于领域自适应的标准模型截面的模型无关测量
Model independent measurements of Standard Model cross sections with Domain Adaptation
摘要：
随着欧洲核子研究中心大型强子对撞机的ATLAS和CMS实验收集的数据量不断增加，希格斯玻色子产生截面的基准和差分测量已经成为以前所未有的精度水平测试标准模型预测的重要工具，以及寻找可以证明物理存在的偏差超出标准模型。这些测量通常被设计为易于与任何当前或未来的理论预测进行比较，为了实现这一目标，重要的是将模型依赖性保持在最低限度。然而，模型依赖性的降低通常是以牺牲测量精度为代价的，从而阻止利用信号提取过程的全部潜力。本文提出了一种基于域自适应机器学习概念的新型方法，该方法允许在信号提取过程中使用复杂的深度神经网络，同时确保测量结果对信号理论建模的依赖性最小。
英文摘要：
With the ever growing amount of data collected by the ATLAS and CMS experiments at the CERN LHC, fiducial and differential measurements of the Higgs boson production cross section have become important tools to test the standard model predictions with an unprecedented level of precision, as well as seeking deviations that can manifest the presence of physics beyond the standard model. These measurements are in general designed for being easily comparable to any present or future theoretical prediction, and to achieve this goal it is important to keep the model dependence to a minimum. Nevertheless, the reduction of the model dependence usually comes at the expense of the measurement precision, preventing to exploit the full potential of the signal extraction procedure. In this paper a novel methodology based on the machine learning concept of domain adaptation is proposed, which allows using a complex deep neural network in the signal extraction procedure while ensuring a minimal dependence of the measurements on the theoretical modelling of the signal.
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。
