Advances in Experimental Particle Physics through Statistical Methodology and Data Analysis

通过统计方法和数据分析在实验粒子物理方面取得进展

• 批准号: 0527090
• 负责人: Karen Kafadar
• 金额: --
• 依托单位: University of Colorado at Denver-Downtown Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-12-01 至 2007-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0527090&HistoricalAwards=false
• 关键词: Advances; Experimental; Particle; Physics; through

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).

经典的统计方法是在20世纪的大部分时间内开发的，用于分析小型至定型的数据集。 功能大大增加的计算设备的普遍性导致了大量数据集的收集和存储。 在许多领域（例如电信和粒子物理学）中，数据集本质上是无限的：随着连接建立连接，进行财务交易，并且颗粒随时间相互作用时，新数据不断出现。 此外，数据可能涉及许多高度相关的变量。例如，关于交易的数据可能包括购买者和供应商的人口统计学变量，除了交易量之外，以及物理实验中粒子的数据涉及速度，动量和质量等特征，所有这些都可能影响其他粒子的特征。大量的大量数据需要开发新的统计方法。需要这种方法的一个重要区域是高能粒子物理。 拟议的研究的目标是开发统计方法和算法，以查看和识别模式，大量数据，具体目标是将这些方法应用于粒子物理实验中的数据中，以及HENCELEAD，以及HENCELEAD，以及：（1）通过实验数据库的新统计方法（2）分析统计学的粒子理论（2）分析（2）分析（2）分析（2）分析粒子理论的进步（2）在国家标准局（现为NIST）的统计工程局和在史蒂尔特·皮克德（Hewlett Packard）公司的微波测试产品和研究部门的统计工程局（现为敏捷）的统计工程局（现为敏捷）中，PI将在体育科学中进行跨学科研究。 （SLAC）Babar合作和伯克利（Lawrence Berkeley实验室和物理学系）。 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拟议的研究包括物理和统计数据的进步。 在物理学中，这项研究应导致与与B梅森（SLAC）相关的特定颗粒衰变反应相关的不确定性的更精确和量化。 这些知识将在PI与Nist-Boulder的物理学家的合作中对剖腹频率标准的不确定性进行合作。 在统计数据中，这项研究应导致新的方法来分析此类实验的大量数据。 更广泛的影响：PI期望这种合作不仅使物理学家对涉及B梅森的衰变反应的理解有益于物理学家使用粒子物理实验中的数据，推进有关标准模型的知识状态的方法，从而导致新的大规模数据集/流的方法，并增强了对计算学院学生的教育体验的新方法。该IGMS项目由国会议员多学科活动办公室（OMA）和数学科学部（DMS）共同支持。