Unsupervised Learning for Identifying Events in Active Target Experiments-猫眼课题宝

DOI：

10.1016/j.nima.2021.165461

发表时间：

2020-08

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

R. Solli;D. Bazin;M. Kuchera;R. Strauss;M. Hjorth-Jensen

本文提出了无监督机器学习方法在主动目标探测器-主动目标时间投影室(AT-TPC)中事件分离问题的新应用(Bradt，2017)。首要目标是在数据分析的早期阶段对类似事件进行分组，从而通过限制不必要事件的计算代价高昂的处理来提高效率。介绍了非监督聚类算法在46 Ar质子共振散射实验粒子径迹二维投影分析中的应用。我们研究了自动编码器神经网络和一个预先训练的VGG16(Simonyan和Zisserman，2015)卷积神经网络的性能。我们研究了来自模拟46Ar实验的数据和来自AT-TPC探测器的真实事件的聚类性能。我们发现，k-Means算法应用于VGG16潜在空间中的模拟数据形成了几乎完美的簇。此外，VGG16+k-Means方法为真实的实验数据找到了高纯度的质子事件簇。探讨了自动编码神经网络的潜在空间聚类在事件分离中的应用。虽然这些网络表现出强劲的性能，但它们的结果存在很大的变异性。

This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection Chamber (AT-TPC)(Bradt, 2017). The overarching goal is to group similar events in the early stages of the data analysis, thereby improving efficiency by limiting the computationally expensive processing of unnecessary events. The application of unsupervised clustering algorithms to the analysis of two-dimensional projections of particle tracks from a resonant proton scattering experiment on 46 Ar is introduced. We explore the performance of autoencoder neural networks and a pre-trained VGG16 (Simonyan and Zisserman, 2015) convolutional neural network. We study clustering performance on both data from a simulated 46 Ar experiment, and real events from the AT-TPC detector. We find that a k-means algorithm applied to simulated data in the VGG16 latent space forms almost perfect clusters. Additionally, the VGG16+ k-means approach finds high purity clusters of proton events for real experimental data. We also explore the application of clustering the latent space of autoencoder neural networks for event separation. While these networks show strong performance, they suffer from high variability in their results.

Unsupervised Learning for Identifying Events in Active Target Experiments

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