Advances in Experimental Particle Physics through Statistical Methodology and Data Analysis

经典的统计方法是在20世纪的大部分时间里发展起来的，用于分析小型到中等规模的数据集。随着计算设备的普及，其功能大大增强，导致了大量数据集的收集和存储。在许多领域，如电信和粒子物理，数据集本质上是无限的：随着连接的建立，金融交易的进行，以及粒子随着时间的推移而相互作用，新的数据不断产生。此外，数据可能涉及许多高度相关的变量；例如，一笔交易的数据除了交易金额外，还可能包括买方和供应商的人口统计变量，物理实验中的粒子数据涉及速度、动量和质量等特征，所有这些都可能影响其他粒子的特征。如此庞大的数据集需要开发新的统计方法。需要这种方法的一个重要领域是高能粒子物理学。该研究的目标是开发用于查看和识别大量数据模式的统计方法和算法，并将这些方法应用于粒子物理实验数据的具体目标，从而导致：(1)通过实验数据库的新应用在粒子物理理论方面取得了进展；(2)分析大量数据集的新统计方法；(3)为计划从事物理科学跨学科研究的学生提供了更好的教育体验。在斯坦福直线加速器中心（SLAC） BaBar合作和伯克利（劳伦斯伯克利实验室和物理系）的首席研究员Robert G. Jacobsen教授的指导下，PI将加强她在粒子物理学方面的本科教育，并在国家标准局（现为NIST）的统计工程部和惠普公司（现为安捷伦）的微波测试产品和研究部门工作。回到丹佛后，她将向统计学和物理学的学生介绍高能物理和统计方法的问题，以分析大量数据集，并将这一经验应用于NIST-Boulder时频部主次频率标准（铯喷泉；五个氢微波激射器，四个商业铯标准）校准模型的不确定性评估问题。智力优势：所提议的研究的具体成果包括物理学和统计学的进展。在物理学中，这项研究将导致与B介子（SLAC）相关的特定粒子衰变反应相关的更精确和量化的不确定性。这些知识将有助于pi与NIST-Boulder的物理学家合作研究铯喷泉频率标准的不确定性。在统计学上，这项研究应该会带来新的方法来分析来自此类实验的大量数据。更广泛的影响：PI期望这次合作不仅有利于物理学家对涉及B介子的衰变反应的理解，而且将为物理学家使用粒子物理实验数据开辟道路，推进关于标准模型的知识状态，导致分析大量数据集/流的新方法，并增强学生在计算统计学方面的教育经验。该IGMS项目由MPS多学科活动办公室（OMA）和数学科学司（DMS）联合支持。

Classical statistical methods were developed during much of the 20th century to analyze small-to-moderatesized data sets. The ubiquity of computing devices with vastly increased capabilities has led to the collection and storage of massive data sets. In many fields, such as telecommunications and particle physics, the data sets are essentially infinite: new data arise continuously as connections are established, financial transactions are conducted, and particles interact over time. Moreover, the data are likely to involve manyhighly correlated variables; e.g., data on a transaction may include demographic variables on both the purchaser and the supplier, in addition to the amount of the transaction, and data on particles in a physics experiment involve characteristics such as velocity, momentum, and mass, all of which can affect the characteristics on other particles.Such massive data sets require the development of new statistical methods. One important area where suchmethods are needed is high-energy particle physics. The goal for the proposed research is to develop statistical methods and algorithms for viewing, and identifying patterns in, massive amounts of data,with the specific goal of applying these methods to data from particle physics experiments, and hencelead to: (1) advances in the theory of particle physics through novel uses of experimental databases, (2) new statistical methods for analyzing massive data sets, and (3) enhanced educational experiences for students who plan to pursue interdisciplinary research in the physical sciences.The PI will enhance her undergraduate education in particle physics and work experience in the Statistical Engineering Division at the National Bureau of Standards (now NIST) and in the microwave test products and research division at Hewlett Packard Company (now Agilent), under the supervision of Professor Robert G. Jacobsen, a lead researcher at Stanford Stanford Linear Accelerator Center (SLAC) BaBar collaborationand in Berkeley (Lawrence Berkeley Laboratory and Department of Physics). Upon returning to Denver, she will introduce students in both statistics and physics to problems in high-energy physics and statistical methodology for the analysis of massive data sets, and apply this experience to the problem of assessment of uncertainty in the calibration models for the primary and secondary frequency standards (cesium fountain; five hydrogen masers, four commercial cesium standards) at NIST-Boulder's Time and Frequency Division.Intellectual Merit:Specific outcomes of the proposed research include advances in both physics and statistics. In physics,this research should lead to greater precision and quantification of uncertainties associated with specific particle decay reactions associated with the B meson (SLAC). This knowledge will be useful in thePI's collaborations with physicists at NIST-Boulder on the uncertainty in the cesium fountain frequency standard. In statistics, this research should lead to new methods for analyzing massive data from such experiments. Broader impacts:The PI expects that this collaboration will benefit not only physicists' understanding of decay reactions involving the B meson but also will lead the way for physicists' use of data from particle physics experiments, advance the state of knowledge about the Standard Model, lead to new methods of analyzing massive data sets/streams, and enhance the educational experience for students in computational statistics. This IGMS project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).