Achieves 15% Noise Reduction in Gravitational Waves Via Learnable Wavelets

Gravitational wave astronomy faces a persistent challenge in distinguishing genuine signals from transient, non-Gaussian noise known as glitches. Arush Pimpalkar, Digvijay Wadekar, and Mark Ho-Yeuk Cheung, from Johns Hopkins University and collaborating institutions, alongside Emanuele Berti, present a novel approach utilising learnable wavelets to efficiently reduce these interfering signals in data from the LIGO-Virgo-KAGRA detectors. Their research demonstrates a significantly more sample-efficient method than traditional convolutional networks, leveraging the inherent wavelet-like structure of glitches to model and mitigate their impact. This advancement is particularly significant as it promises to improve the sensitive volume of gravitational wave searches by up to 15% for high-mass binary systems, ultimately enhancing our ability to detect and characterise these elusive cosmic events.

WaveletNet tackles gravitational wave glitch identification

Scientists have unveiled WaveletNet, a novel wavelet-based neural network architecture designed to identify and mitigate non-Gaussian noise within gravitational wave data. Their research demonstrates a significantly more sample-efficient method than traditional convolutional networks, leveraging the inherent wavelet-like structure of glitches to model and mitigate their impact. This advancement is particularly significant as it promises to improve the sensitive volume of gravitational wave searches by up to 15% for high-mass binary systems, ultimately enhancing our ability to detect and characterise these elusive cosmic events.

WaveletNet for Glitch Reduction in Gravitational Waves offers

Researchers observed that glitches, often mimicking high-mass black hole signals, exhibit a distinct wavelet-like structure, prompting a novel methodological approach. The study pioneered a framework leveraging simple neural networks to determine the optimal wavelet family for modelling glitches within LIGO-Virgo-KAGRA O3 data. This simplicity dramatically improves sample efficiency compared to convolutional neural networks (CNNs), allowing effective training with limited data. Experiments employed the TIER method as a foundation, demonstrating how WaveletNet can enhance the performance of any gravitational wave search pipeline.
The team downweighted potential gravitational wave candidates exhibiting noisy strain regions in their immediate vicinity, specifically within tcandidate ± O(10) seconds. WaveletNet functions in a modular fashion, providing a score that can be integrated with the pipeline’s existing detection statistic score for candidate events. This innovative integration allows for a refined ranking of potential signals, reducing false positives and increasing detection confidence. Tests utilising candidates from the IAS-HM search pipeline revealed a significant improvement in sensitive volume by up to 15% for high-mass, asymmetric binaries. Researchers harnessed Morlet wavelets as an approximate generative model, parameterising them with a lightweight multi-layer perceptron (MLP) to infer central frequency and temporal duration. This approach prioritises data efficiency over architectural flexibility, enabling robust performance even with limited examples of specific glitch morphologies.

WaveletNet boosts gravitational wave detection sensitivity by reducing

Scientists have developed a wavelet-based network, termed WaveletNet, to identify and reduce non-Gaussian noise within gravitational wave data. The research addresses a challenge in gravitational wave astronomy: the need for sample efficiency when processing long data segments, a common limitation of traditional convolutional networks (CNNs). Experiments revealed that glitches mimicking high-mass black hole signals exhibit a wavelet-like structure, prompting the team to leverage this property by employing simple networks to learn optimal wavelets for modelling glitches in LIGO-Virgo-KAGRA O3 data. The team measured the impact of WaveletNet on the sensitive volume, a key metric for evaluating detection efficiency, and found improvements of up to 15% for high-mass, asymmetric binaries.

This breakthrough delivers a significant enhancement in the ability to detect faint gravitational wave signals by effectively filtering out noise. Results demonstrate that WaveletNet’s modular framework provides an output score that can be integrated into existing detection pipelines, downweighting candidates with noisy strain regions. Combining wavelet-based features with pipeline summary statistics yields a substantial improvement in the inverse false-alarm rate (IFAR). Data shows that the ratio of the updated IFAR to the original value indicates a noticeable suppression of noisy events.