Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of the Intersection of Confidence Intervals Rule

Abstract

Gravitational-wave data (discovered first in 2015 by the Advanced LIGO interferometers and awarded by the Nobel Prize in 2017) are characterized by non-Gaussian and non-stationary noise. The ever-increasing amount of acquired data requires the development of efficient denoising algorithms that will enable the detection of gravitational-wave events embedded in low signal-to-noise-ratio (SNR) environments. In this paper, an algorithm based on the local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule for the filter support selection is proposed to denoise the gravitational-wave burst signals from core collapse supernovae. The LPA-RICI denoising method’s performance is tested on three different burst signals, numerically generated and injected into the real-life noise data collected by the Advanced LIGO detector. The analysis of the experimental results obtained by several case studies (conducted at different signal source distances corresponding to the different SNR values) indicates that the LPA-RICI method efficiently removes the noise and simultaneously preserves the morphology of the gravitational-wave burst signals. The technique offers reliable denoising performance even at the very low SNR values. Moreover, the analysis shows that the LPA-RICI method outperforms the approach combining LPA and the original intersection of confidence intervals (ICI) rule, total-variation (TV) based method, the method based on the neighboring thresholding in the short-time Fourier transform (STFT) domain, and three wavelet-based denoising techniques by increasing the improvement in the SNR by up to 118.94% and the peak SNR by up to 138.52%, as well as by reducing the root mean squared error by up to 64.59%, the mean absolute error by up to 55.60%, and the maximum absolute error by up to 84.79%.

Detection of Non-Stationary GW Signals in High Noise From Cohen’s Class of Time–Frequency Representations Using Deep Learning

“…Another approach involves denoising techniques that do not require information about the astrophysical properties of the underlying signals. These techniques, including the total-variation-based technique [7] and the technique based on the combination of the local polynomial approximation (LPA) and the relative intersection of confidence intervals (RICI) [8], have recently attracted increasing interest, with applications to denoising of BBH and core-collapse supernova (CCSN) signals. Although these techniques are efficient in noise reduction, they have to be coupled with other algorithm pipelines to enable GW events detection.…”

Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder

“…[37,38]) and LSTM. An algorithm implemented in [39], based on the local polynomial approximation combined with the relative intersection of confidence intervals rule for the filter support selection is applied to denoise the GW burst signals emitted during core collapse supernovae events.…”

Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach

“…Next, the same simulations have been performed on the real-life gravitational-wave signal z RS [56,57]. The optimal solution for the RTwIST algorithm obtained by the FSM gives: α + = 0.946, β + = 0.92, p + = 1, δ + t = 0.91, δ + f = 0.56, Γ + = 0.142, Υ + = 0.189.…”