将模拟MicroBooNE事件上的粒子分类训练扩展到多个GPU
Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs
摘要：
在液氩时间投影室（LArTPC）中微子探测器（如费米实验室的MicroBooNE探测器）中进行的测量具有大的高保真事件图像。深度学习技术在照片的分类任务中非常成功，但由于事件的大小，将其应用于LArTPC事件图像具有挑战性。这些检测器中的事件通常比经典挑战中发现的图像大两个数量级，例如识别MNIST数据库中包含的手写数字或ImageNet数据库中的对象识别。理想情况下，训练将发生在整个事件数据的许多实例上，而不是从事件数据中裁剪的感兴趣区域的许多实例。然而，这样的努力导致了非常长的训练周期，这减缓了对新网络架构和超参数扫描的探索，以提高分类性能。我们目前的研究扩展LArTPC分类问题的多个架构，跨越多个节点。研究是在MicroBooNE探测器上模拟事件进行的。我们强调，优化网络或从任何结果中提取物理学超出了本研究的范围。使用了太平洋西北国家实验室的机构计算和橡树岭国家实验室领导计算设施的SummitDev机器。据我们所知，这是第一次将最先进的卷积神经网络用于粒子物理及其伴随的计算技术应用于DOE领导课程设施。我们预计，未来几十年美国高能物理（HEP）旗舰计划-地下中微子实验（DUNE）LArTPC计划将特别受益。
英文摘要：
Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors, such as the MicroBooNE detector at Fermilab, feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to LArTPC event images is challenging, due to the large size of the events. Events in these detectors are typically two orders of magnitude larger than images found in classical challenges, like recognition of handwritten digits contained in the MNIST database or object recognition in the ImageNet database. Ideally, training would occur on many instances of the entire event data, instead of many instances of cropped regions of interest from the event data. However, such efforts lead to extremely long training cycles, which slow down the exploration of new network architectures and hyperparameter scans to improve the classification performance. We present studies of scaling a LArTPC classification problem on multiple architectures, spanning multiple nodes. The studies are carried out on simulated events in the MicroBooNE detector. We emphasize that it is beyond the scope of this study to optimize networks or extract the physics from any results here. Institutional computing at Pacific Northwest National Laboratory and the SummitDev machine at Oak Ridge National Laboratory's Leadership Computing Facility have been used. To our knowledge, this is the first use of state-of-the-art Convolutional Neural Networks for particle physics and their attendant compute techniques onto the DOE Leadership Class Facilities. We expect benefits to accrue particularly to the Deep Underground Neutrino Experiment (DUNE) LArTPC program, the flagship US High Energy Physics (HEP) program for the coming decades.
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