RUI: Digging Deep for New Physics with the CMS Experiment

世纪的一项重大科学成就是粒子物理学标准模型的发展。标准模型是一个成功的理论，它与几十年来涉及弱相互作用、电磁相互作用和强相互作用的实验观测相一致。在欧洲核子研究中心的大型强子对撞机（LHC）上发现的希格斯玻色子完成了标准模型粒子的预测，并提供了产生基本粒子质量的机制。然而，目前标准模型的公式并没有解释希格斯玻色子质量的观测值，这使得发现标准模型之外的物理成为LHC实验的主要目标，包括在这个项目中进行的涉及紧凑μ子螺线管（CMS）的实验。这项工作将专注于寻找新的物理学，以及在2020年代中期的高亮度LHC运行期间升级CMS实验的硅基外部跟踪器的发展。这项研究还将使本科生参与贝瑟尔大学、费米实验室和欧洲核子研究中心的CMS数据分析和探测器硬件项目。主要分析目标是使用破纪录的CMS数据集搜索矢量类夸克（VLQ）对产生。将开发几种深度机器学习算法来识别底夸克、顶夸克、W、Z和希格斯玻色子的强子衰变，这些粒子都被重建为具有独特内部结构的喷流。通过这些深度学习技术，VLQ对产生搜索可以显着改进，以包括识别和质量重建。具体来说，这项工作将使用图像识别机器学习技术来开发用于HL-LHC研究的喷流识别算法。此外，将对升级硅跟踪器模块的设计特征和组装程序进行广泛的开发。Bethel CMS小组将对现有探针台进行更新，以表征硅传感器，并将参与费米实验室的模块生产测试。Bethel集团还参与了一些推广和教育活动，通过探测器展览的图尔斯之旅接触高中生和公众，并通过年度数据分析学校和暑期实践教程为CMS分析做好准备。伯特利小组还将开发基于调查的开放数据分析练习，适用于高级现代物理课程和/或高级实验室课程，与其他大学共享。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

A major scientific achievement of the 20th century was the development of the Standard Model of particle physics. The Standard Model is a successful theory, agreeing with decades of experimental observations involving weak, electromagnetic, and strong interactions. The Higgs boson discovery at the Large Hadron Collider (LHC) at CERN completes the suite of predicted Standard Model particles and provides a mechanism for generating elementary particle masses. However, the current formulation of the Standard Model does not account for the observed value of the Higgs boson mass, making discovery of physics beyond the Standard Model a primary goal of the LHC experiments, including those being carried out in this project involving the Compact Muon Solenoid (CMS). This work will focus on searches for new physics, as well as upgrade developments toward the silicon-based outer tracker for the CMS experiment during the High-Luminosity LHC run in the mid-2020s. This research will also engage undergraduate students in CMS data analysis and detector hardware projects at Bethel University, Fermilab, and CERN. The primary analysis goal is to search for vector-like quark (VLQ) pair production with the record-breaking CMS dataset. Several deep machine learning algorithms will be developed to identify hadronic decays of bottom quarks, top quarks, W, Z, and Higgs bosons, which are all reconstructed as jets with unique internal structure. With these deep learning techniques, VLQ pair production searches can be dramatically improved to include identification and mass reconstruction. Specifically, this work will use image recognition machine learning techniques to develop jet identification algorithms for HL-LHC studies. Additionally, extensive development will be made on design features and assembly procedures for upgraded silicon tracker modules. The Bethel CMS group will characterize silicon sensors with updates to an existing probe station and will participate in module production tests at Fermilab. The Bethel group is also engaged with several outreach and education activities, reaching high school students and the general public through tours of detector exhibits and preparing new students and collaborators for CMS analysis via an annual Data Analysis School and hands-on summer tutorials. The Bethel group will also develop inquiry-based Open Data analysis exercises appropriate for upper-level modern physics courses and/or advanced laboratory courses to be shared with other universities.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.