在$\langle E_ν\rangle \sim 6$ GeV的MINERvA实验中使用机器学习进行中性π介子重建
Neutral pion reconstruction using machine learning in the MINERvA experiment at $\langle E_ν\rangle \sim 6$ GeV
摘要：
本文提出了一种新的中性π介子重建方法，利用2013-2017年收集的MINERvA数据进行语义分割的机器学习技术，平均中微子能量为$6$ GeV。语义分割将中性π介子重建的纯度从71%提高到89%，重建效率提高了约40%。我们证明了我们的方法在带电电流中性π介子生产分析，其中一个单一的中性π介子重建。这种技术适用于现代跟踪量热计，如新一代的液氩时间投影室，暴露于中微子束与$\langle E_ν\rangle$之间的1-10 GeV。在这样的实验中，它可以促进与电磁簇射相关的电离命中的识别，从而使得能够改进由$ν_μ \rightarrow ν_{e}$出现引起的充电电流$ν_e$事件的重建。
英文摘要：
This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013-2017, with an average neutrino energy of $6$ GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two gammas from 71\% to 89\% and improves the efficiency of the reconstruction by approximately 40\%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with $\langle E_ν\rangle$ between 1-10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current $ν_e$ events arising from $ν_μ \rightarrow ν_{e}$ appearance.
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