arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.05659physics.ins-detphysics.data-an
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基于机器学习的双相氙时间投影室脉冲分类方法

A machine learning-based methodology for pulse classification in dual-phase xenon time projection chambers

P. Brás, F. Neves, A. Lindote, A. Cottle, R. Cabrita, E. Lopez Asamar, G. Pereira, C. Silva, V. Solovov, M. I. Lopes

摘要:

机器学习技术现在已经在实验粒子物理学中得到了很好的应用,允许以新的和独特的方式分析探测器数据。粒子观测站的信号识别是一项重要的数据处理任务,可以使用这种方法进行改进。本文旨在探讨的好处,一个专门的机器学习方法可能提供的双相惰性气体时间投影室的信号分类。一个完整的方法,从探索性的数据分析使用高斯混合模型和功能的重要性排名的基础上的神经网络和随机森林的标准实现的专用预测模型的建设,验证使用未标记的模拟数据从LZ实验作为代理真实数据。在这项工作中开发的预测模型的全局分类精度估计为> 99.0%,这是对使用相同数据测试的传统算法的改进。聚类分析的结果也被用来识别由错误计算的信号特性引起的数据异常,表明这种方法也可以用于数据监测。

英文摘要:

Machine learning techniques are now well established in experimental particle physics, allowing detector data to be analysed in new and unique ways. The identification of signals in particle observatories is an essential data processing task that can potentially be improved using such methods. This paper aims at exploring the benefits that a dedicated machine learning approach might provide to the classification of signals in dual-phase noble gas time projection chambers. A full methodology is presented, from exploratory data analysis using Gaussian mixture models and feature importance ranking to the construction of dedicated predictive models based on standard implementations of neural networks and random forests, validated using unlabelled simulated data from the LZ experiment as a proxy to real data. The global classification accuracy of the predictive models developed in this work is estimated to be >99.0%, which is an improvement over conventional algorithms tested with the same data. The results from the clustering analysis were also used to identify anomalies in the data caused by miscalculated signal properties, showing that this methodology can also be used for data monitoring.

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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