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）上进行ATLAS实验的一个小组提供支持。大型强子对撞机和大型粒子探测器设施ATLAS是利用NSF和世界各地其他机构的资金建造的基础科学工具。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）中的最后一种粒子，该模型解释了宇宙中形成物质的已知粒子的存在及其之间的作用力。这一努力取得了成功。实验的下一步是寻找超越标准模型（BSM）的物理学证据，例如，可以解释构成宇宙质量的神秘“暗物质”的存在。大型强子对撞机刚刚开始运行3，事件样本显着增加。随着事件统计数据的增加，BSM物理学的证据可能会出现。从分析上讲，新墨西哥州小组正在使用ATLAS探测器寻找BSM物理。这些研究包括一个明确的ATLAS测量的分支比的罕见衰变的中性Bs介子成两个μ子。这个测量很有趣，因为LHC的早期数据产生了与标准模型预测相矛盾的值，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暗示，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，新墨西哥州小组领导了一项对像素跟踪检测器的研究，该研究描述了该检测器在下一次运行结束时的性能，并为该时代的像素操作提供了指导方针。像素探测器对于研究寿命短的粒子（如小组感兴趣的中性Bs介子）以及更广泛的ATLAS发现科学计划至关重要。新墨西哥州研究活动的更广泛影响包括机器学习的进步。这种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这一领域的权威，并将能够培训其他人。机器学习在虚拟助手、医学诊断、DNA分类和其他研究领域有着广泛的应用，在这些领域中，弱信号必须从广泛的背景中灵敏地分离出来。该小组的研究是他们赞助的QuarkNet项目的跳板，包括LHC大师班、教师实验室研究和新墨西哥州科学设施的技术图尔斯之旅。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.