Experimental Particle Physics

该奖项将为在瑞士日内瓦粒子物理实验室 CERN 的大型强子对撞机 (LHC) 上从事 ATLAS 实验的团队提供支持。 LHC 机器和大型粒子探测器 ATLAS 是利用 NSF 和世界各地其他机构的资金作为基础科学工具建造的。他们的主要目标之一是找到希格斯玻色子，这是历史上成功的“标准模型”（SM）中的最后一个粒子，该模型解释了形成宇宙物质的已知粒子的存在以及它们之间的作用力。这项努力取得了成功。实验的下一步是寻找标准模型（BSM）之外的物理学证据，例如，这些证据可能解释构成宇宙大部分质量的神秘“暗物质”的存在。 LHC 刚刚开始第 3 次运行，事件样本显着增加。 BSM 物理学的证据可能会随着更高的事件统计数据而出现。从分析角度来说，新墨西哥小组正在使用 ATLAS 探测器寻找 BSM 物理现象。这些研究包括对中性 Bs 介子罕见衰变成两个 μ 子的分支比率进行明确的 ATLAS 测量。这种测量很有趣，因为大型强子对撞机的早期数据产生了与标准模型的预测存在张力的值，任何重大偏差都是新 BSM 物理学的预兆。在即将开始的数据运行中，该小组正在研究软件基础设施，以探测轻子味道异常的迹象，并改进 B 物理的触发。该软件现在专注于机器学习技术。在 ATLAS 的硬件方面，新墨西哥小组领导了一项对 Pixel 跟踪探测器的研究，该研究描述了该探测器在下一次运行结束时的性能能力，并为这个时代的 Pixel 操作提供了指南。像素探测器对于研究寿命较短的粒子（例如该小组感兴趣的中性 Bs 介子）以及更广泛的 ATLAS 发现科学计划至关重要。新墨西哥小组研究活动的更广泛影响包括机器学习的进步。此类分析工具的开发是新墨西哥州数据分析所固有的，该小组的初级成员正在成为该领域的权威，并将能够培训其他人。机器学习在虚拟助理、医疗诊断、DNA 分类和其他研究领域有着广泛的应用，在这些领域中，微弱的信号必须从广泛的背景中敏感地分离出来。该小组的研究是他们赞助的 QuarkNet 项目的跳板，包括大型强子对撞机大师班、教师实验室研究以及新墨西哥州科学设施的技术参观。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

This award will provide support to a group working on the ATLAS experiment at the Large Hadron Collider (LHC) at CERN, a particle physics laboratory in Geneva, Switzerland. The LHC machine and ATLAS, a large particle detector facility, were built as basic science tools using funds from NSF and other agencies around the world. One of their primary objectives was to find the Higgs Boson, the last particle in the historically successful "Standard Model" (SM) that accounts for so much of the existence of, and forces between, known particles forming the matter in the universe. This effort has been successful. The next step in the experiments is to look for evidence for physics Beyond the Standard Model (BSM) that might, for instance, account for the presence of the mysterious "Dark Matter" that makes up so much of the mass of the universe. The LHC is just starting Run 3 with significantly increased event samples. It is possible that evidence for BSM physics could emerge with the higher event statistics. Analytically, the New Mexico group is searching for BSM physics using the ATLAS detector. These studies include a definitive ATLAS measurement of the branching ratio of the rare decay of the neutral Bs meson into two muons. This measurement is interesting because the early data from LHC yielded a value in tension with predictions of the Standard Model, and any significant deviations are a harbinger of new BSM physics. In the upcoming data run which just started, the group is working on software infrastructure to probe for hints of lepton flavor anomaly and improved triggering for B physics. This software is now focused on Machine Learning techniques. On the hardware side of ATLAS, the New Mexico group leads a study of the Pixel tracking detector that characterizes the performance capabilities of this detector through the end of the next run and yields guidelines for Pixel operations in this era. The pixel detector is critical to the studies of particles with short lifetimes such as the neutral Bs mesons of interest to the group, as well as for the broader ATLAS program of discovery science. The broader impacts of the New Mexico group's research activities include advances in machine learning. The development of such analytical tools is intrinsic to the New Mexico data analyses, and the group's junior members are becoming authoritative in this domain and will be able to train others. Machine learning has applications in virtual assistants, medical diagnosis, DNA classification, and other research domains where weak signals must be sensitively separated from extensive background. The group's research serves as a springboard for QuarkNet projects that they sponsor, including LHC masterclasses, laboratory research for teachers, and technical tours of New Mexico scientific facilities.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.