...
首页> 外文期刊>Nuclear Instruments & Methods in Physics Research >Optimal proton trapping strategy for a neutron lifetime experiment
【24h】

Optimal proton trapping strategy for a neutron lifetime experiment

机译:中子寿命实验的最佳质子俘获策略

获取原文
获取原文并翻译 | 示例

摘要

In a neutron lifetime experiment conducted at the National Institute of Standards and Technology, protons produced by neutron decay events are confined in a proton trap. In each run of the experiment, there is a trapping stage of duration τ. After the trapping stage, protons are purged from the trap. A proton detector provides incomplete information because it goes dead after detecting the first of any purged protons. Further, there is a dead time δ between the end of the trapping stage in one run and the beginning of the next trapping stage in the next run. Based on the fraction of runs where a proton is detected, I estimate the trapping rate λ by the method of maximum likelihood. I show that the expected value of the maximum likelihood estimate is infinite. To obtain a maximum likelihood estimate with a finite expected value and a well-defined and finite variance, I restrict attention to a subsample of all realizations of the data. This subsample excludes an exceedingly rare realization that yields an infinite-valued estimate of λ. I present asymptotically valid formulas for the bias, root-mean-square prediction error, and standard deviation of the maximum likelihood estimate of λ for this subsample. Based on nominal values of λ, and the dead time δ, I determine the optimal duration of the trapping stage τ by minimizing the root-mean-square prediction error of the estimate.
机译:在美国国家标准技术研究院进行的中子寿命实验中,由中子衰变事件产生的质子被限制在质子阱中。在每次实验中,都有一个持续时间为τ的诱捕阶段。在捕获阶段之后,将质子从陷阱中清除。质子检测器提供的信息不完整,因为它在检测到所有清除的质子中的第一个后就会死亡。此外,在一次运行中的捕集阶段的结束与下一次运行中的下一个捕集阶段的开始之间存在停滞时间δ。基于检测到质子的运行分数,我通过最大似然法估计捕获率λ。我表明最大似然估计的期望值是无限的。为了获得具有有限期望值和明确定义的有限方差的最大似然估计,我将注意力集中在数据所有实现的子样本上。该子样本不包括产生λ的无穷大估计的极其罕见的实现。对于该子样本,我给出了偏差,均方根预测误差和λ的最大似然估计的标准偏差的渐近有效公式。基于λ的标称值和停滞时间δ,我通过最小化估计的均方根预测误差来确定陷波级τ的最佳持续时间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号