Deep convolutional neural networks (CNNs) show strong promise for analyzing scientific data in many domains including particle imaging detectors such as a liquid argon time projection chamber (LArTPC). Yet the high sparsity of LArTPC data challenges traditional CNNs which were designed for dense data such as photographs. A naive application of CNNs on LArTPC data results in inefficient computations and a poor scalability to large LArTPC detectors such as the Short Baseline Neutrino Program and Deep Underground Neutrino Experiment. Recently, submanifold sparse convolutional networks (SSCNs) have been proposed to address this class of challenges. We report their performance on a three-dimensional (3D) semantic segmentation task on simulated LArTPC samples. In comparison with standard CNNs, we observe that the computation memory and wall-time cost for inference are reduced by a factor of 364 and 33, respectively, without loss of accuracy. The same factors for 2D samples are found to be 93 and 3.1, respectively. Using SSCN and public 3D LArTPC samples, we present the first machine learning-based approach to the reconstruction of Michel electrons, a standard candle for energy calibration in LArTPC due to their very well-understood energy spectrum. We find a Michel electrons identification efficiency of 93.9% and a 96.7% purity. Reconstructed Michel electron clusters yield 95.4% in average pixel clustering efficiency and 95.5% in purity. The results are compelling in showing the strong promise of scalable data reconstruction technique using deep neural networks for large scale LArTPC detectors.

中文翻译：

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用于稀疏局部密集液氩时间投影室数据的可扩展深度卷积神经网络

深度卷积神经网络 (CNN) 显示出在许多领域分析科学数据的强大前景，包括粒子成像探测器，如液氩时间投影室 (LArTPC)。然而，LArTPC 数据的高度稀疏性对传统的 CNN 提出了挑战，这些 CNN 专为照片等密集数据而设计。CNN 在 LArTPC 数据上的幼稚应用会导致计算效率低下，并且对大型 LArTPC 检测器（例如短基线中微子程序和深层地下中微子实验）的可扩展性较差。最近，已经提出了子流形稀疏卷积网络（SSCN）来解决此类挑战。我们报告了他们在模拟 LArTPC 样本上的三维 (3D) 语义分割任务上的表现。与标准 CNN 相比，我们观察到推理的计算内存和挂墙时间成本分别减少了 364 和 33 倍，而不会损失准确性。发现 2D 样本的相同因子分别为 93 和 3.1。使用 SSCN 和公共 3D LArTPC 样本，我们提出了第一个基于机器学习的重建米歇尔电子的方法，米歇尔电子是 LArTPC 中能量校准的标准蜡烛，因为它们的能谱非常容易理解。我们发现米歇尔电子识别效率为 93.9%，纯度为 96.7%。重建的米歇尔电子簇平均像素簇效率为 95.4%，纯度为 95.5%。结果令人信服地展示了使用深度神经网络进行大规模 LArTPC 检测器的可扩展数据重建技术的强大前景。