Identifying deterministic signals in simulated gravitational wave data: algorithmic complexity and the surrogate data method

We describe the application of complexity estimation and the surrogate data method to identify deterministic dynamics in simulated gravitational wave (GW) data contaminated with white and Coloured noises. The surrogate method uses algorithmic complexity as a discriminating statistic to decide if noisy data contain a statistically significant level of deterministic dynamics (the GW signal). The results illustrate that the complexity method is sensitive to a small amplitude simulated GW background (SNR down to 0.08 for white noise and 0.05 for coloured noise) and is also more robust than commonly used linear methods (autocorrelation or Fourier analysis).