Noise subtraction from KAGRA O3GK data using Independent Component Analysis

Abstract

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.

Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run

“…As previously described, there are a wide variety of additional glitch subtraction methods that are already available [32,[36][37][38][39][41][42][43][44][45][46][47][48][49] or may be ready for use by the next observing run. There are also updates already implemented to BayesWave [19,31] that were not available for use in O3.…”

“…Many techniques have been developed to model glitches using gravitational-wave strain data alone, including modelled Bayesian approaches [19,[28][29][30][31][32] and some based on machine learning [33,34]. Likewise, techniques that utilize the large number of additional auxiliary sensors at each observatory to model sources of noise with analytic methods [35][36][37][38][39][40][41][42][43][44] and machinelearning approaches [45][46][47][48][49] have been proposed.…”