Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis

Noise subtraction is a crucial process in gravitational wave (GW) data analysis to improve the sensitivity of interferometric detectors. While linear noise coupling has been extensively studied and successfully mitigated using methods such as Wiener filtering, subtraction of non-linearly coupled and non-stationary noise remains a significant challenge. In this work, we propose a novel independent component analysis (ICA)-based framework designed to address non-linear coupling in noise subtraction. Building upon previous developments, we derive a method to estimate general quadratic noise coupling while maintaining computational transparency compared to machine learning approaches. The proposed method is tested with simulated data and real GW strain data from KAGRA. Our results demonstrate the potential of this framework to effectively mitigate complex noise structures, providing a promising avenue for improving the sensitivity of GW detectors.