论文标题
使用独立的组件分析从Kagra O3GK数据中减去噪声减法
Noise subtraction from KAGRA O3GK data using Independent Component Analysis
论文作者
论文摘要
2020年4月,卡格拉(Kagra)与GEO〜600检测器(O3GK)进行了首次科学观察,持续了两个星期。根据噪声预算的估计,低频带中的悬架控制噪声和中间频带中的声学噪声被确定为主要贡献。在这项研究中,我们表明可以通过使用一种称为独立组件分析(ICA)的方法来降低离线数据分析中的噪声。在这里,ICA模型是从Ikagra数据分析中研究的频率依赖性中扩展的,而耦合的线性和平稳性仍然假定。通过使用最佳的证人传感器,在实际观察数据中减轻了这两个主要贡献。我们还分析了整个两周数据的传递函数的稳定性,以研究如何实际使用当前的减法方法将其用于重力波搜索。
In April 2020, KAGRA conducted its first science observation in combination with the GEO~600 detector (O3GK) for two weeks. According to the noise budget estimation, suspension control noise in the low frequency band and acoustic noise in the middle frequency band are identified as the dominant contribution. In this study, we show that such noise can be reduced in offline data analysis by utilizing a method called Independent Component Analysis (ICA). Here the ICA model is extended from the one studied in iKAGRA data analysis by incorporating frequency dependence while linearity and stationarity of the couplings are still assumed. By using optimal witness sensors, those two dominant contributions are mitigated in the real observational data. We also analyze the stability of the transfer functions for whole two weeks data in order to investigate how the current subtraction method can be practically used in gravitational wave search.