机器学习辅助CREDO实验的噪声过滤和信号分类

Ł. Bibrzycki, L. Bibrzycki, D. Alvarez-Castillo, O. Bar, D. Góra, P. Homola, P. Kovács, M. Niedźwiecki, M. Piekarczyk, K. Rzecki, J. Stasielak, S. Stuglik, O. Sushchov, A. Tursunov, Credo
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引用次数: 1

摘要

宇宙射线极端分布天文台(CREDO)收集的智能手机数据的丰富程度大大超过了人工分析的能力。因此,有必要采用有效的方法来排除非宇宙射线噪声,并识别由广泛的空气阵雨引起的信号。为了解决这些问题,我们讨论了基于卷积神经网络的伪信号抑制方法和基于常见统计分类器的互补粒子识别方法,以及它们的集成扩展。这些方法都是基于监督学习的,所以我们需要提供CREDO数据集的一个有代表性的子集来进行训练和验证。根据这种方法,超过2300张图片被选中,并由5名评委手工标记。这些图像被分成点、轨迹、蠕虫(统称为信号)和伪影类。然后对图像进行灰度化和背景去除预处理。为了抑制伪信号,提出了一种能够区分伪信号和信号的基于cnn的二值分类器。该分类器以Daubechies小波变换图像的形式输入数据。在宇宙射线信号分类的情况下,考虑了众所周知的基于特征的分类器。作为特征描述符,我们使用带有与图像总亮度相关的附加特征的泽尼克矩。对于伪影剔除问题,我们获得了99%的准确率。对于4类信号分类,表现最好的分类器识别率达到88%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning aided noise filtration and signal classification for CREDO experiment
The wealth of smartphone data collected by the Cosmic Ray Extremely Distributed Observatory (CREDO) greatly surpasses the capabilities of manual analysis. So, efficient means of rejecting the non-cosmic-ray noise and identification of signals attributable to extensive air showers are necessary. To address these problemswe discuss a Convolutional Neural Network-basedmethod of artefact rejection and complementarymethod of particle identification based on common statistical classifiers aswell as their ensemble extensions. These approaches are based on supervised learning, so we need to provide a representative subset of the CREDO dataset for training and validation. According to this approach over 2300 images were chosen and manually labeled by 5 judges. The images were split into spot, track, worm (collectively named signals) and artefact classes. Then the preprocessing consisting of luminance summation of RGB channels (grayscaling) and background removal by adaptive thresholding was performed. For purposes of artefact rejection the binary CNN-based classifier was proposed which is able to distinguish between artefacts and signals. The classifier was fed with input data in the form of Daubechies wavelet transformed images. In the case of cosmic ray signal classification, the well-known feature-based classifiers were considered. As feature descriptors, we used Zernike moments with additional feature related to total image luminance. For the problem of artefact rejection, we obtained an accuracy of 99%. For the 4-class signal classification, the best performing classifiers achieved a recognition rate of 88%.
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