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Characterization of Radon Progeny in EXO-200 Using Machine Learning Algorithms.

机译:使用机器学习算法表征EXO-200中的Rad子代。

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摘要

Neutrinoless double beta decay (0nubetabeta) is a rare, second-order process that occurs in certain isotopes for which beta decay is energetically forbidden. EXO-200 is a 0nubetabeta experiment with 110 kg of active liquid xenon (LXe) isotopically enriched in 136Xe. EXO-200 detects events using a combination of scintillation and ionization signals, which allows for excellent particle discrimination. However, events with a low ionization signal cannot be fully characterized with the current analysis framework. To fill in these gaps, we introduce a boosted decision tree regressor as a new tool to characterize events in the detector. We focus on alpha decays of 222Rn and its progeny, which have low ionization signals that often fall below the threshold for position reconstruction. Using information gained from this technique, we confirm previous results for the 218Po ion velocity and improve previous results for the 218Po ionization fraction. We also investigate events that occur near the walls of the vessel. These events have no ionization signal and therefore cannot be characterized with any existing technique in the analysis framework. By investigating these events we determine that they are not distributed uniformly throughout the detector, which may point to charging up of the plastics inside the LXe vessel or a "hot spot'' on the plastic due to contamination during cleaning and installation.
机译:中微子双β衰变(0nubeta)是一种罕见的二阶过程,发生在某些同位素上,因此强烈禁止β衰变。 EXO-200是一个0nubetabeta实验,其中110kg的活性液态氙(LXe)同位素富含136Xe。 EXO-200使用闪烁和电离信号的组合来检测事件,从而实现出色的颗粒识别。但是,使用当前的分析框架无法完全表征具有低电离信号的事件。为了填补这些空白,我们引入了增强型决策树回归器作为表征检测器中事件的新工具。我们关注222Rn及其后代的α衰变,这些衰变具有低电离信号,通常低于位置重建的阈值。使用从该技术获得的信息,我们确认218Po离子速度的先前结果,并改善218Po电离分数的先前结果。我们还将调查在容器壁附近发生的事件。这些事件没有电离信号,因此无法使用分析框架中的任何现有技术来表征。通过调查这些事件,我们确定它们在整个检测器中分布不均匀,这可能表明LXe容器内的塑料充满或由于清洁和安装过程中的污染而导致塑料上的“热点”。

著录项

  • 作者

    Smith, Erica.;

  • 作者单位

    Drexel University.;

  • 授予单位 Drexel University.;
  • 学科 High energy physics.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 107 p.
  • 总页数 107
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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