论文标题
使用深度学习技术来搜索微型酮中的小酮低能过量，> 3 $σ$敏感性
Using Deep Learning Techniques to Search for the MiniBooNE Low Energy Excess in MicroBooNE with > 3$σ$ Sensitivity
论文作者
论文摘要
本论文描述了针对微酮实验开发的分析，以研究在迷你酮检测器中观察到的电子样事件的异常过量。这里研究的假设是，小酮异常代表电子中微子的外观。使用新颖的深度学习和标准算法技术的汞合金，该分析重建并鉴定出高度纯净的带电当前的准弹性muon中微子和电子中微子相互作用的样本。本文描述了分析链中的步骤，并为每个步骤提供了对最终预测信心的数据对仿真比较。当在$νe$外观（例如模型）的上下文中解释时，此分析预测，使用$ 7 \ times10^{20} $质子在微酮数据的目标上，将排除标准模型波动排除标准模型波动。
This thesis describes an analysis developed for the MicroBooNE experiment to investigate an anomalous excess of electron-like events observed in the MiniBooNE detector. The hypothesis investigated here is that the MiniBooNE anomaly represents appearance of electron neutrinos. Using an amalgam of novel Deep Learning and standard algorithmic techniques this analysis reconstructs and identifies a highly pure sample of charged current quasi-elastic muon neutrino and electron neutrino interactions. This thesis describes the steps in the analysis chain and provides data-to-simulation comparisons for each step that establish confidence in the final prediction. When interpreted in the context of a $νe$ appearance like model, this analysis predicts a 3.2$σ$ sensitivity to exclude a standard model fluctuation which would appear as a MiniBooNE like anomaly using $7\times10^{20}$ protons on target of MicroBooNE Data.
