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arXiv 1104.3248astro-ph.IMcs.LGphysics.data-an
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声中微子探测中的信号分类

Signal Classification for Acoustic Neutrino Detection

Friedrich-Alexander-Universität Erlangen-Nürnberg(埃尔朗根-纽伦堡弗里德里希-亚历山大大学);Erlangen Centre for Astroparticle Physics(埃尔朗根天体粒子物理中心)

M. Neff, G. Anton, A. Enzenhöfer, K. Graf, J. Hößl, U. Katz, R. Lahmann, C. Richardt

摘要:

本文主要研究深海声中微子探测中的信号分类问题。在深海中,瞬态信号的背景是非常多样的。匹配滤波等方法不足以区分类中微子信号和具有类似特征的其他瞬态信号,这些信号构成了深海环境中中微子探测的声学背景。基于机器学习算法的分类系统进行了分析,目的是找到一个强大而有效的方法来执行这项任务。对于一个经过良好训练的模型,强分类器(如随机森林和提升树)使用提取的信号特征作为输入,并利用密集的传感器集群而不是单个传感器,可以实现1%的测试误差。

英文摘要:

This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrino-like signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environment. A classification system based on machine learning algorithms is analysed with the goal to find a robust and effective way to perform this task. For a well-trained model, a testing error on the level of one percent is achieved for strong classifiers like Random Forest and Boosting Trees using the extracted features of the signal as input and utilising dense clusters of sensors instead of single sensors.

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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