Application of independent component analysis to the iKAGRA data

Abstract

We apply independent component analysis (ICA) to real data from a gravitational wave detector for the first time. Specifically, we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates the usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal results for the case of environmental noise acting on the strain channel linearly.

Noise subtraction from KAGRA O3GK data using Independent Component Analysis

“…( 9) coincides with the Wiener filtering for two channels and it turns out to be a optimal subtraction method for the coupling (6) in the context of ICA. In our previous work [23], we showed that the above discussion is generalized to arbitrary number of witness channels x i (t) (i = 1, ..., n) and confirmed that the performance of this method corresponding to the Wiener filtering [24] was better than FastICA in subtracting seismic noise from iKAGRA data. Based on this result, we extend the model of mixing matrix (6) by incorporating the effect of time delay, or the frequency dependence in the following.…”

“…While the principle of ICA based on the statistical independence is so general that it is applicable to the nonlinearly coupled system [25], it makes the calculation more transparent compared to the machine learning. In the previous study [23], we demonstrated that the seismic noise can be partially subtracted by the simplest model of ICA. Note that the model assuming instantaneous linear mixing, was implemented in two different ways and the optimal case turned out to be the time domain Wiener filtering [24,25].…”

“…This is achieved through the Gram-Schmidt orthogonalization in terms of two point correlation between witness channels, as was done in Ref. [23] for the simplest instantaneous coupling. In this frequency dependent case, the cleaned strain data is given by ỹ0…”

“…It is worthwhile mentioning that the strain sensitivity of LIGO Hanford during O2 was significantly improved by utilizing the Wiener filtering with the auxiliary sensors, e.g., photodiodes that monitor beam motion and its size for beam jitter [13]. As for KAGRA data, application of Independent Component Analysis (ICA) [20,21,22] was investigated with the initial KAGRA (iKAGRA) strain data and seismometers [23]. ICA is a method of signal processing which separates mixture of signals into statistically independent components by making use of non-Gaussianity of the sources.…”

Performance of the KAGRA detector during the first joint observation with GEO 600 (O3GK)

“…The acoustic noise limited the O3GK sensitivity in some frequencies between 100 Hz and 400 Hz. Therefore, the following approaches are planned to reduce them in the next observation: (1) Introduction of soundproofing material at the experimental site to reduce the acoustic field, (2) Mitigation of scattered light propagation using new optical baffles and beam dumps inside and outside the vacuum enclosure in addition to the currently 21/37 installed ones [7], (3) Acoustic contamination in the DARM signal subtracted by independent component analysis performed in the iKAGRA study [12].…”