Implementation for Lightweight Deep Learning for Anomaly Detection and Denoising on Gravitational Waves

As gravitational wave astronomy has advanced, the need for effective and quick signal processing has never been more critical. New detectors such as Laser Interferometer Gravitational-Wave Observatory (LIGO) produces huge volumes of data, which poses a significant challenge to identify genuine astrophysical events amidst transient noise. With this in view, this study proposes a new, efficient deep-learning architecture designed to process gravitational wave signals. Motivated by the WaveNet model, our method uses dilated convolutions to precisely model long-term dependencies in the data ensuring that subtle characteristics are captured. In addition, higher-order recurrent layers like Long Short-Term Memory(LSTM) networks are also used to precisely model temporal characteristics so that accuracy is preserved with enhanced anomaly detection and noise removal. Our experimental validation shows that this method has achieved a Root Mean Square Error (RMSE) of 0.00228, a Residual Signal Ratio (RSR) of 0.08798, a Peak Signal-to-Noise Ratio (PSNR) of 33.34 dB, and an average volume error of 0.03965. These performance metrics show the framework’s ability to operate in real time with minimal computational overhead is a key requirement given the massive datasets involved in gravitational wave science. Also, by an explicit consideration of initial and boundary conditions, our scheme exhibits not only stability for the current missions but also scalability for future detection missions, like Laser Interferometer Space Antenna (LISA), allowing these missions to enhance their sensitivity and reliability. Our study demonstrates the development of state-of-the-art deep learning methods to surmount the specific obstacles concerning gravitational wave detection, paving the way for real-time processing of astrophysical data and an improved understanding of the Universe.