Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks
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DOI：
10.1109/icmla.2016.0160
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Evan Racah;Seyoon Ko;Peter Sadowski;W. Bhimji;C. Tull;Sang-Yun Oh;P. Baldi;Prabhat
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文献类型：
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作者：
Evan Racah;Seyoon Ko;Peter Sadowski;W. Bhimji;C. Tull;Sang-Yun Oh;P. Baldi;Prabhat
粒子物理学的实验产生了大量的数据，这些数据必须由物理学家团队进行分析和解释。这种分析通常是探索性的，科学家无法在进行实验之前列举可能的信号类型。因此，用于总结、聚类、可视化和分类高维数据的工具是必不可少的。在这项工作中，我们展示了有意义的物理内容可以通过使用深度神经网络将原始数据转换为学习的高级表示来揭示，并在大亚湾中微子实验中进行了测量作为案例研究。我们进一步展示了卷积深度神经网络如何在不同类别的物理事件中提供有效的分类过滤器，准确率超过97%，明显优于其他机器学习方法。
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.