CAREER: Physics at the Interplay between the Higgs Boson and the Top Quark

十年前，大型强子对撞机(LHC)的启动开启了迄今为止难以获得的能量体系研究的新纪元。希格斯玻色子的发现是大型强子对撞机计划的第一个主要亮点，该计划确立了标准模型(SM)的预测粒子含量和相互作用是完整的。尽管取得了这样的成功，但人们普遍认为SM是一个不完整的理论。正在进行的研究必须确定这个新的希格斯玻色子是否是SM的玻色子，而新的实验数据对于发现大自然是否以及如何选择扩展SM是必不可少的。这些研究包括新的测量和对新物理的搜索，主要是使用目前最高可用的质心能量记录的数据。物理分析活动形成了一个全面的计划，以扩展希格斯玻色子性质的表征。该奖项将通过采用新的机器学习技术，将这种对物理的探索推向SM之外的极限。该奖项开发的创新：使用图形神经网络(GNN)分析大型强子对撞机ATLAS实验产生的海量数据，将推动希格斯玻色子研究的发现潜力。该奖项还将利用大型强子对撞机和GNN技术的数据将教育和外联工作扩大到当地高中和普通公众。该奖项将在涉及顶夸克相关产生(TTH和TH)的希格斯玻色子通道中寻找Beyond the SM(BSM)物理。它还将进一步研究希格斯衰变为双光子和四夸克通道。这些都是罕见的过程，需要该奖项正在开发的新技术。这些渠道预计将对BSM物理产生强烈的制约。GNN的新用途还处于初级阶段，但涉及到将大型强子对撞机的数据重新分类为图形结构。然后，这种图表结构可以被输入到神经网络中，神经网络可以有效地选择出感兴趣的非常罕见的事件，揭示BSM。这项奖励还将进一步发展目前正在为高亮度大型强子对撞机(HL-LHC)做准备的ATLAS探测器的升级。这项工作将集中在带状追踪器的升级和数据采集系统，这需要一个新的软件框架，以满足HL-LHC的严格性能要求。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The start-up of the Large Hadron Collider (LHC) a decade ago ushered in a new era of research in hitherto inaccessible energy regimes. The discovery of the Higgs boson is the first major highlight of the LHC program, which established that the predicted particle content and interactions of the Standard Model (SM) are complete. Despite this success, the SM is widely believed to be an incomplete theory. Ongoing studies must determine whether or not this new Higgs boson is that of the SM, and new experimental data are essential to discover if and how Nature has chosen to extend the SM. These studies include new measurements and searches for new physics, primarily using data recorded at the highest currently available center-of-mass energy. The physics analysis activities form a comprehensive program to extend characterization of Higgs boson properties. This award will push the limits of this search for physics beyond the SM by employing new Machine Learning techniques. The innovation being developed by this award: using Graph Neural Networks (GNN) to analyze the massive amount of data being produced by the ATLAS experiment at the LHC, will push the discovery potential in the Higgs boson study. The award will also expand an education and outreach effort using data from the LHC and GNN techniques into local area high schools and the general public. This award will search for Beyond the SM (BSM) physics in the Higgs boson channels involving top-quark-associated production (ttH and tH). It will also further study Higgs decay into two-photon and four-quark channels. These are rare processes and will require the new techniques being developed by this award. These channels are expected to put strong constraints on BSM physics. The novel use of GNN is in a nascent state but involves re-classifying LHC data into a graph structure. This graph structure can then be fed into a neural network which can in effect select out the very rare events that are of interest, revealing BSM. This award will also further develop the upgrade to the ATLAS detector currently in preparation for the High Luminosity LHC (HL-LHC). The work will focus on the Strip Tracker upgrade and the Data Acquisition system, which requires a new software framework in order to meet the stringent performance requirements of the HL-LHC.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.