# Application of the independent component analysis to the iKAGRA data

## TL;DR

This paper demonstrates the first application of independent component analysis (ICA) to real gravitational wave detector data, showing it can effectively identify and enhance signals amidst environmental noise.

## Contribution

The study introduces the use of ICA on actual iKAGRA data and compares two implementations, highlighting its potential for noise reduction in gravitational wave detection.

## Key findings

- ICA successfully recovers injected signals with improved SNR.
- Correlation-based ICA performs best when environmental noise is linear.
- First application of ICA to real gravitational wave data.

## Abstract

We apply the independent component analysis (ICA) to the 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 usefulness of this method. Among the two implementations of ICA used here, we find the correlation method yields the optimal result for the case environmental noises act on the strain channel linearly.