The Higgs boson and the top quark in the ATLAS detector: gateway to new physics

欧洲核子研究中心的大型强子对撞机(LHC)在寻求通过其基本成分了解宇宙的过程中，取得了重要的成果，证实了当前的粒子物理理论--标准模型(SM)。希格斯玻色子的发现是一项了不起的成就：它解释了基本粒子是如何获得质量的。尽管SM非常准确地描述了量子尺度上的基本粒子物理定律，但它未能解释我们宇宙中的主要宇宙学现象。发现一个延伸到SM之外的“新物理学”的迹象将是一项开创性的发现，并将引导人们对自然进行更全面的描述。 测量希格斯玻色子与最重的物质粒子顶夸克的相互作用，是探索新物理学特征的一个很有前途的途径。被称为汤川耦合的相互作用强度对新物理非常敏感，与理论预测的任何偏差都将揭示非SM现象。我领导了观测希格斯玻色子产生的背景评估工作，该工作与大型强子对撞机的多用途ATLAS探测器有关。TTH产生模式是可以直接进入顶夸克Yukawa耦合的物理过程。然而，从大型强子对撞机数据中提取TTH信号具有极大的挑战性；主要的背景过程很难准确建模。传统上使用蒙特卡罗(MC)技术进行的模拟，由于理论上的限制，本质上是有偏差的。 我建议部署先进的机器学习工具来更精确地测量顶夸克汤川耦合，以便充分开发即将到来的LHC Run 3(2021-2023)的潜力。生成性对抗网络(GANS)是一种很有前途的深度学习模型，在高能物理中有相关的应用。我提出了一种逐步实现GANS的方法，以减少巨大的理论不确定性，并减少TTH分析对MC模拟的依赖。这是为2026年开始的高亮度大型强子对撞机计划做准备，届时具有高统计量的MC的需求几乎是10倍。GaN产生的碰撞将比MC产生得更快，并且计算强度要小得多。 为高亮度大型强子对撞机做准备还需要升级探测器，我建议利用我在粒子探测器鉴定方面的专业知识加入约克大学/多伦多集群的工作，该集群负责生产和测试未来ATLAS内跟踪器的500个传感器单元(硅和电子设备)。约克大学最适合作为专门的网站，对未通过标准质量保证测试的部件进行详细评估和回收。我建议设计和建造评估实验室，并监督生产过程中的重新评估和恢复工作。

In the quest to understand the universe through its elementary constituents, CERN's Large Hadron Collider (LHC) has delivered important results confirming the current theory of particle physics, the Standard Model (SM). The discovery of the Higgs boson was a remarkable achievement: it explained how elementary particles acquired their masses. Despite being a very accurate description of the fundamental particle physics laws at the quantum scale, the SM fails to explain major cosmological phenomena in our universe. Finding a sign of “new physics” extending beyond the SM would be a ground-breaking discovery, and a guide toward a more complete description of nature. A promising gateway to probe for signatures of new physics is the measurement of the Higgs boson's interaction with the heaviest particle of matter: the top quark. The interaction strength, called Yukawa coupling, is very sensitive to new physics, and any deviation with respect to the theoretical prediction would uncover non-SM phenomena. I led the effort on the background evaluation towards the observation of the Higgs boson production in association with a top quark pair, or ttH, with the multipurpose ATLAS detector at the LHC. The ttH production mode is the physics process that can give direct access to the top quark Yukawa coupling. However, extracting the ttH signal from the LHC data is extremely challenging; the main background processes are very difficult to accurately model. The simulations, traditionally performed using Monte Carlo (MC) techniques, are intrinsically biased due to theoretical limitations. I propose to deploy advanced machine learning tools to measure with better precision the top quark Yukawa coupling in order to exploit the full potential of the incoming LHC Run 3 (2021 - 2023). Generative Adversarial Networks (GANs) are promising deep learning models with relevant applications in high energy physics. I propose a gradual approach to implement GANs for mitigating the large theoretical uncertainties and make the ttH analysis less reliant on MC simulations. This is in preparation for the High-Luminosity LHC program starting 2026, where the needs of MC with high statistics is almost multiplied by ten. GAN-generated collisions would be faster to produce than MC and much less computationally intensive. Preparing for the High-Luminosity LHC also requires detector upgrades and I propose to use my expertise in particle detector characterization to join the effort of the York University/Toronto cluster, which is in charge of producing and testing 500 sensor units (silicon and electronics) of the future ATLAS inner tracker. York University is best suited to be the dedicated site for detailed evaluation and recovery of components failing standard quality assurance tests. I propose to design and construct the evaluation laboratory, and oversee the reevaluation and recovery efforts during production.