Comparison of various methods to extract ringdown frequency from gravitational wave data
Comparison of various methods to extract ringdown frequency from gravitational wave data
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DOI：
10.1103/physrevd.99.124032
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发表时间：
2018-11
影响因子：
5
通讯作者：
H. Nakano;T. Narikawa;K. Oohara;K. Sakai;H. Shinkai;H. Takahashi;Takahiro Tanaka;N. Uchikata;S. Yamamoto;Takahiro Yamamoto
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文献类型：
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作者：
H. Nakano;T. Narikawa;K. Oohara;K. Sakai;H. Shinkai;H. Takahashi;Takahiro Tanaka;N. Uchikata;S. Yamamoto;Takahiro Yamamoto
在致密天体合并的最后阶段，引力波的振铃部分告诉我们强引力的本质，它可以用来检验引力理论。然而，衰荡波形在很短的时间内以几个周期逐渐消失，因此对于引力波数据分析来说，提取衰荡频率及其阻尼时间尺度是具有挑战性的。在这里，我们建议建立一套引力波的模拟数据来比较各种方法的性能，以检测黑洞的准正常模式。本文给出了以下五种方法的初步比较结果：（1）带振铃部分的普通匹配滤波（MF-R）方法，（2）合并和振铃部分的匹配滤波（MF-MR）方法，（3）Hilbert-Huang变换（HHT）方法，（4）自回归模型（AR）方法，（5）神经网络（NN）方法。在比较了他们的表现之后，我们讨论了我们未来的项目。
The ringdown part of gravitational waves in the final stage of merger of compact objects tells us the nature of strong gravity which can be used for testing the theories of gravity. The ringdown waveform, however, fades out in a very short time with a few cycles, and hence it is challenging for gravitational wave data analysis to extract the ringdown frequency and its damping time scale. We here propose to build up a suite of mock data of gravitational waves to compare the performance of various approaches developed to detect quasi-normal modes from a black hole. In this paper we present our initial results of comparisons of the following five methods; (1) plain matched filtering with ringdown part (MF-R) method, (2) matched filtering with both merger and ringdown parts (MF-MR) method, (3) Hilbert-Huang transformation (HHT) method, (4) autoregressive modeling (AR) method, and (5) neural network (NN) method. After comparing their performance, we discuss our future projects.