RUI: Digging Deep for New Physics with the CMS Experiment

20世纪的一项重大科学成就是粒子物理标准模型的发展。标准模型是一个成功的理论，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察相一致。欧洲核子研究中心大型强子对撞机(LHC)上的希格斯玻色子的发现完善了标准模型粒子的预测，并提供了一种产生基本粒子质量的机制。然而，目前的标准模型公式没有考虑到希格斯玻色子质量的观测值，这使得发现超出标准模型的物理成为大型强子对撞机实验的主要目标，包括在这个涉及紧凑型Muon螺线管(CMS)的项目中进行的实验。这项工作将集中在寻找新的物理，以及升级发展的硅基外跟踪器的CMS实验期间，在高亮度大型强子对撞机运行在2020年代中期。这项研究还将使伯特利大学、费米拉实验室和欧洲核子研究中心的本科生参与CMS数据分析和探测器硬件项目。主要的分析目标是用破纪录的CMS数据集搜索类矢量型夸克(VLQ)对的产生。将开发几种深度机器学习算法来识别底部夸克、顶部夸克、W、Z和Higgs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注。有了这些深度学习技术，VLQ对产生式搜索可以显著改进，包括识别和大规模重建。具体地说，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，还将在升级的硅跟踪器模块的设计功能和组装程序方面进行广泛的开发。Bethel CMS小组将对现有探测站进行更新，以表征硅传感器，并将参与Fermilab的模块生产测试。伯特利小组还参与了几项外联和教育活动，通过参观探测器展品接触到高中生和普通公众，并通过年度数据分析学校和实践暑期教程为新学生和合作者做好CMS分析的准备。伯特利小组还将开发基于探究的开放数据分析练习，适用于与其他大学共享的高级现代物理课程和/或高级实验室课程。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。

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.