Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
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DOI：
10.1103/physrevd.103.092003
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发表时间：
2021-05-14
影响因子：
5
通讯作者：
Zhang, C.
中科院分区：
文献类型：
--
作者：
Abratenko, P.;Alrashed, M.;Zhang, C.
介绍了MicroBooNE开发的用于多目标分类的卷积神经网络--多粒子识别网络(MPID)。MPID提供在液态Ar时间投影室单一读出平面中相互作用包括e(-)、伽马、mU(-)、pi(+/-)和质子的概率。该网络扩展了MicroBooNE以前开发的单粒子识别网络[卷积神经网络应用于液态Ar时间投影室中的中微子事件，R.Acciarri等人。J.Instrum.12，P03011(2017)]。MPID将围绕重建交互顶点裁剪的图像或仅包含与重建顶点连接的活动的图像作为输入，从而使工具避免了在顶点查找和粒子聚类方面的低效。该网络是MicroBooNE基于深度学习的.e搜索分析的重要组成部分。在本文中，我们介绍了网络的设计，训练和性能的模拟和来自MicroBooNE探测器的数据。
We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an e(-), gamma, mu(-), pi(+/-), and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep-learning-based.e search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.