On Optimal Parameters for ICI-Based Adaptive Filtering Applied to the GWs in High Noise

The detection of the gravitational-wave (GW) events from the noisy measured data requires reliable filtering techniques. This paper provides the performance analysis of the intersection of confidence intervals (ICI) and local polynomial approximation (LPA) based techniques when applied to the filtering of the GW signals in the real-life, low signal-to-noise-ratio (SNR) conditions. The techniques' sensitivities to the parameter changes are studied by evaluating the performance indices values. The analysis shows that the proper selection of the algorithm parameters is important in obtaining the optimal filtering performance, with the technique based on the relative intersection of confidence intervals (RICI) having an additional parameter that may compensate for the sub-optimally selected ICI parameter. Moreover, the evaluation of the results obtained by applying the optimally tuned filtering techniques suggests that the RICI-based technique provides better performance in filtering the GW signals, even compared to the tested wavelet-based techniques.