CAREER: Probing the High Energy Frontier With the CMS Experiment

对描述最小亚原子长度上相互作用的物理学的完整理解是难以捉摸的。通过欧洲核子研究中心大型强子对撞机（LHC）的高能质子-质子碰撞，我们可以瞬间打开一扇通往已知最高能量的窗户，同时探测这些微小的尺寸，从而提供了在其他无法进入的领域直接研究基础物理的机会。这项工作包括在堪萨斯大学（KU）的一个研究计划，该计划使用LHC CMS实验中现有和未来的数据，在这些高能尺度上面对开放的问题。Rogan的KU小组是LHC紧凑μ子螺线管（CMS）实验的成员，该项目中的大多数活动都与此有关。该项目有两个主要组成部分：CMS收集的数据的分析和解释，以及CMS探测器本身。在这两种情况下，都有一些元素集中在已经收集的数据和实验的操作上，其他人期待着分析数据的新方法和未来对探测器的升级。这项工作的很大一部分是对标准模型之外与新对称性和暗物质相关的物理学进行新的全面搜索，其中包括开发一种新的分析范式，将无监督机器学习的元素纳入对新物理学的搜索，并计划使用现有CMS数据集进行概念验证演示。该小组还在为CMS开发新的精确定时探测器，这将为事件重建引入一个额外的维度，促进对涉及长寿命粒子的新物理的搜索。该小组还在研究现有CMS电磁量热计的时序重建性能，目标是通过改进的重建算法减轻分辨率下降。KU小组还专注于两项针对公众和KU学术界的计划。与KU本科生一起，该小组正在开发“幻想物理”推广项目，学生可以在网上以幻想运动的形式与由选定的粒子物理实验组成的团队竞争。该小组还开发了一个跨部门的“学习机器学习”论坛，其中包括讲座，教程和研讨会，为有兴趣将当代机器学习纳入其研究的KU学术界成员提供服务。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

A complete understanding of the physics describing interactions at the smallest, subatomic lengths is elusive. Through the high-energy proton-proton collisions of CERN’s Large Hadron Collider (LHC), we can momentarily open a window to the highest energies known and simultaneously probe these tiny sizes, providing the opportunity to directly study fundamental physics in an otherwise inaccessible regime. This work includes a program of research at the University of Kansas (KU) which confronts open questions at these high-energy scales using existing and future data from the CMS experiment at the LHC. Rogan’s KU group are members of the Compact Muon Solenoid (CMS) experiment at the LHC, with most of the activities included in this project related to this effort. The project has two primary components: the analysis and interpretation of data collected by CMS, and the CMS detector itself. In both cases, there are elements that focus on already collected data and the operation of the experiment, with others looking forward to new approaches to analyzing data and future upgrades to the detector. A significant portion of this work is for a new comprehensive search for physics beyond the Standard Model related to new symmetries and Dark Matter, which includes development of a novel analysis paradigm that incorporates elements of unsupervised machine learning into searches for new physics, with proof-of-concept demonstrations planned with the existing CMS dataset. The group is also developing new, precision timing detectors for CMS, which will introduce an additional dimension to event reconstruction, facilitating searches for new physics involving long-lived particles. The group is also studying the timing reconstruction performance of the existing CMS electromagnetic calorimeter, with the goal of mitigating degradations in resolution through an improved reconstruction algorithm.The KU group is also focused on two initiatives targeting the public and the KU academic community. With KU undergraduates, the group is developing the "Fantasy Physics" outreach project, where students can compete with teams comprised of selected particle physics experiments in an online fantasy-sports-inspired format. The group also developed an inter-departmental "Learning Machine Learning" forum, which includes lectures, tutorials, and seminars serving members of the KU academic community who are interested in incorporating contemporary machine learning in their research. The group is also active in disseminating its research through public talks and outreach events.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.