Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE
Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE
复制标题
使用米歇尔电子和 0 样本进行电磁簇射重建和能量验证，用于 MicroBooNE 中基于深度学习的分析
DOI：
10.1088/1748-0221/16/12/t12017
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发表时间：
2021
影响因子：
1.3
通讯作者：
Barr, G.
中科院分区：
文献类型：
--
作者：
Abratenko, P.;An, R.;Anthony, J.;Arellano, L.;Asaadi, J.;Ashkenazi, A.;Balasubramanian, S.;Baller, B.;Barnes, C.;Barr, G.
本文介绍了在基于MicroBooNE深度学习的低能电子搜索中使用的电子和光子（簇射）的电磁活动的重建。重建算法结合了传统和基于深度学习的技术来估计淋浴能量。我们使用两个ν μ源数据样本验证了这些预测：带电/中性电流与末态中性π介子的相互作用和带电电流相互作用，其中μ介子在检测器内停止并衰变产生Michel电子。中性π介子样本和Michel电子样本都证明了数据与模拟之间的一致性。此外，绝对簇射能量尺度被证明是一致的，与每个样品的相关物理常数：中性π介子质量峰和米歇尔能量截止。
This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two ν μ-sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.