Classification of glitch waveforms in gravitational wave detector characterization
Classification of glitch waveforms in gravitational wave detector characterization
复制标题
引力波探测器表征中毛刺波形的分类
DOI：
10.1088/1742-6596/243/1/012006
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
B. Matkarimov
中科院分区：
文献类型：
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作者：
S. Mukherjee;R. Obaid;B. Matkarimov
本文描述了一种新的快速有效的算法来分类像LIGO这样的引力波探测器中出现的信号波形。这样的波形分类是有用的和重要的检测器的特性，以及了解毛刺的分析管道，检测信号从天体物理爆发和紧凑的对象inspiral源。基于离散参数的毛刺的分类已经由作者早些时候报道。在目前的研究中，已经开发出一种新的特征挖掘方法，该方法使用基于毛刺波形形状的附加信息。由于真实的引力波数据中存在的独特结构，这一直是一个具有挑战性的问题。本文介绍了模拟结果以及真实的数据。研究表明，该方法是快速和有效的分类毛刺噪声。
The paper describes a new fast and efficient algorithm to classify waveforms from signals that arise in gravitational wave detectors like LIGO. Such waveform classification is useful and important for detector characterization as well as for understanding glitches seen in the analysis pipelines that detect signals from astrophysical burst and compact object inspiral sources. Classification of glitches based on discrete parameters has been reported earlier by the author. In the current study, a new feature-mining approach has been developed that uses the additional information based on shape of the glitch waveforms. This has been a challenging problem because of the unique structures present in the real gravitational wave data. The paper presents results from simulations as well as real data. Studies show that the proposed method is fast and efficient in classifying glitches in noise.