CAREER: Development of a Fast Hardware Tracker for ATLAS and Massively Parallel Computing Strategies for Integrated Research, Education, and Outreach in Particle Physics

有了这个奖项，Neubauer教授将继续执行一个五年计划，为处理、分析和可视化利用大型强子对撞机(LHC)的ATLAS探测器获得的海量数据制定战略。将与芝加哥大学的人员合作，为快速硬件跟踪器(FTK)系统开发硬件，作为对目前ATLAS触发系统的升级。该系统旨在利用感兴趣事件产生的独特跟踪特征，对于保持ATLAS触发器的良好效率和操作稳定性至关重要。大型强子对撞机ATLAS实验收集的数据将以新的方式进行分析，试图阐明电弱对称破缺(EWSB)背后的未知物理，并在TeV尺度上发现新的物理。多元分析和科学图形处理器计算的课程材料将跨越部门界限，将为一个新的研究生级别的专题课程开发，高级分析技术。最后，为国家科学基金会赞助的Enlist-L计划开发的课程将通过使用技术得到更广泛的传播，从而增加服务不足的个人和社区的参与，包括芝加哥地区的高危青年和听力障碍者。该项目还将通过积极参与开放科学网格，加强现有的基础设施，以支持美国需要数据密集型计算的协作科学。

With this award, Professor Neubauer will pursue a five-year plan to develop strategies for processing, analyzing, and visualizing vast quantities of data obtained with the ATLAS detector at the Large Hadron Collider (LHC). In collaboration with personnel at the University of Chicago, hardware will be developed for a fast hardware tracker (FTK) system as an upgrade to the current ATLAS trigger system. Designed to exploit unique tracking signatures produced by events of interest, this system will be crucial for maintaining good efficiency and operational stability of the ATLAS trigger. Data collected by the ATLAS experiment at the LHC will be analyzed in novel ways to try to elucidate the unknown physics behind electroweak symmetry breaking (EWSB) and discover new physics at the TeV scale. Course materials in multivariate analysis and scientific GPU computing that will cut across departmental boundaries will be developed for a new special topics graduate-level class, Advanced Analysis Techniques. Finally, curriculum developed for the NSF sponsored EnLiST-L program (Entrepreneurial Leadership in Science Teaching and Learning) will be more widely disseminated through the use of technology, thereby increasing the engagement of underserved individuals and communities, including at-risk youth in the Chicago area and the hearing-impaired. The project will also enhance existing infrastructure to support collaborative science requiring data-intensive computing in the U.S. through active participation in the Open Science Grid.