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CAREER: Optimization of a large water Cherenkov detector for the Deep Underground Science and Engineering Laboratory

CAREER: Optimization of a large water Cherenkov detector for the Deep Underground Science and Engineering Laboratory
职业:深部地下科学与工程实验室大型水切伦科夫探测器的优化
批准号:
0847150
负责人:
Christopher Walter
金额:
$56.21万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-06-15 至 2015-09-30

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英文摘要
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Neutrinos may hold the imprint of actions in the beginning of the universe that determined the properties of the world today. Foremost among the questions it is hoped neutrinos may shed light on is 'Why is today's universe dominated by matter'? Studying neutrino oscillation with future powerful beams will allow testing the standard neutrino model and the origin of neutrino mass. Studying proton decay can directly probe energy scales at the Grand Unified Scale, and the observation of a galactic supernova would supply a treasure trove of data which would test models in particle, nuclear and astrophysics.This award will fund an R&D program to use data and Monte Carlo from the Super-Kamiokande detector to optimize a 500-kiloton scale water Cherenkov (WC) detector sited at DUSEL for long-baseline neutrino oscillation studies, along with searches for proton decay and astrophysical neutrinos. By leveraging the knowledge the PI has gained in the last decade of working with the world's only multi kiloton-scale WC detector, he plans to optimize the design of a large future detector, maximizing the mass and physics reach while minimizing the resources needed. The Broader Impacts of this project include involving undergraduate students who will be trained on the cutting edge of techniques used in large particle-astrophysics detectors. It is proposed to extend the scope of the extremely successful physics REU program at the TUNL nuclear laboratory on the Duke campus to also offer high energy physics projects to the students who are attending the program.
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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
供应链管理中的稳健型(Robust)策略分析和稳健型优化(Robust Optimization )方法研究
  • 批准号:
    70601028
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    7.0万元
  • 批准年份:
    2006
  • 负责人:
    王明征
  • 依托单位: