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arXiv 2103.06391physics.ins-dethep-ex
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基于深度学习的液氩时间投影室原始波形感兴趣区域寻找器

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

ArgoNeuT Collaboration, R. Acciarri, B. Baller, V. Basque, C. Bromberg, F. Cavanna, D. Edmunds, R. S. Fitzpatrick, B. Fleming, P. Green, C. James, I. Lepetic, X… 展开作者

ArgoNeuT Collaboration, R. Acciarri, B. Baller, V. Basque, C. Bromberg, F. Cavanna, D. Edmunds, R. S. Fitzpatrick, B. Fleming, P. Green, C. James, I. Lepetic, X. Luo, O. Palamara, G. Scanavini, M. Soderberg, J. Spitz, A. M. Szelc, L. Uboldi, M. H. L. S. Wang, W. Wu, T. Yang

摘要:

液氩时间投影室(LArTPC)探测器技术具有测量太阳和超新星产生的低能中微子的特性和寻找极低能量的奇异物理的出色能力。为了实现这些物理目标,识别和重建每条TPC导线上记录的波形中的信号至关重要。在本文中,我们报告了一种新的算法的基础上,一维卷积神经网络(CNN),以寻找感兴趣的区域(ROI)的原始波形。我们使用ArgoNeuT实验的数据,结合改进的噪声缓解程序和模拟事件的更现实的数据驱动的噪声模型来测试该算法。这种深度学习ROI查找器在提取小信号方面表现出良好的性能,并且在$\sim$ 0.03-0.1 MeV的低能量区域中,效率大约是传统算法的两倍。这种方法为利用LArTPC探索低能物理提供了巨大的潜力。

英文摘要:

The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this paper, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of $\sim$0.03-0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.

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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