Mock data challenge for finding ringdown gravitational waves (2018)
- This mock data challenge was started as a
a part of the project in the innovative area "Gravitational wave physics and astronomy: Genesis" (PI: Takahiro Tanaka) , which is funded by JSPS (Japan Society for Promoting Sciences) 2017-2022. This test suit is mainteind by the group A01 "Test of gravity theories using gravitational wave".
- The data were prepared for comparing several new methods for extracting "ringdown part" of gravitational wave after merger of binary black holes.
The signal data are provided with insprial and merger parts of coalescing black holes which bases the numerical simulated templates, and ringdown part was injected after the merger.
In order to implement one's method for future testing gravitational theories, the ringdown part are shifted from those of general relativity. That is, you can not use the information of inspiral part for extracting the ringdown part.
- The data are provided in two types with solutions.
( original web page )
The difference of set A/B is the method for creating data. The details are explained in the article (below).
- Each data is of 3-second h(t), with the sampling rate 4096.
Each data file is a format of [time h_+(t) h_x(t)] , and its solution (information) is prepared with separate files.
- The injected signal of inspiral and merger parts are from those of SXS project. The signal mock data of gravitational wave were injected assuming the advanced LIGO's sensitivity curve with 5 each of (overall) SNR=60, 30, 20. The SNR for the ringdown part turned out to be roughly 1/5 -- 1/3 of these values.
Comparison of various methods to extract ringdown frequency from gravitational wave data
Hiroyuki Nakano, Tatsuya Narikawa, Ken-ichi Oohara, Kazuki Sakai, Hisa-aki Shinkai, Hirotaka Takahashi, Takahiro Tanaka, Nami Uchikata, Shun Yamamoto, and Takahiro Yamamoto
[arXiv:1811.06443] (submitted to Phys. Rev. D)
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 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.
page updated by Hisaaki Shinkai, 2018-Nov-08