Neutral pion reconstruction using machine learning in the experiment at 〈E ν 〉 6 GeV
Neutral pion reconstruction using machine learning in the experiment at 〈E ν 〉 6 GeV
复制标题
在 ØE Ø 6 GeV 实验中使用机器学习重建中性π介子
DOI：
10.1088/1748-0221/16/07/p07060
复制
发表时间：
2021
影响因子：
1.3
通讯作者：
Caceres, G.
中科院分区：
文献类型：
--
作者：
Ghosh, A.;Yaeggy, B.;Galindo, R.;Ahmad Dar, Z.;Akbar, F.;Ascencio, M.V.;Bashyal, A.;Bercellie, A.;Bonilla, J.L.;Caceres, G.
本文利用2013-2017年收集的MINERvA数据，利用语义分割的机器学习技术，提出了一种新的中性介子重构方法，平均中微子能量为6 GeV。语义分割使两个γ - s的中性介子重构纯度从70.7±0.9%提高到89.3±0.7%，重构效率提高了约40%。我们在带电电流中性介子产生分析中证明了我们的方法，其中重建了单个中性介子。该技术适用于现代跟踪量热计，如新一代液氩时间投影室，暴露于< E ν>在1-10 GeV之间的中微子束中。在这样的实验中，它可以促进与电磁雨相关的电离撞击的识别，从而能够改进由ν μ→ν出现引起的带电电流ν e事件的重建。
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 γs from 70.7±0.9% to 89.3±0.7% 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< E ν> 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→ ν e appearance.