arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.12743physics.ins-dethep-exnucl-ex
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基于深度神经网络的液氩时间投影室增强信号处理

Augmented Signal Processing in Liquid Argon Time Projection Chambers with a Deep Neural Network

Haiwang Yu, Mary Bishai, Wenqiang Gu, Meifeng Lin, Xin Qian, Yihui Ren, Andrea Scarpelli, Brett Viren, Hanyu Wei, Hongzhao Yu, Kwang Min Yu, Chao Zhang

摘要:

液态氩时间投影室(LArTPC)是一种先进的中微子探测器技术,广泛应用于最近和即将到来的加速器中微子实验中。它具有低能量阈值和高空间分辨率,可以全面重建事件拓扑结构。在当前一代LArTPC中,记录的数据由漂移电离电子导线上的感应信号产生的导线上的数字化波形组成,其也可以被视为带电粒子轨迹的二维(2D)(时间与导线)投影图像。对于这样的成像检测器,一个关键步骤是信号处理,其从记录的2D图像重建原始电荷投影。我们首次在LArTPC信号处理中引入深度神经网络,以改善信号感兴趣区域的检测。通过结合领域知识(例如,匹配来自多个线平面的信息)和深度学习,该方法显示出比传统方法显著的改进。这项工作详细介绍了方法,软件工具,并与现实的检测器模拟评估的性能。

英文摘要:

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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