Enhancing Gravitational-Wave Detection from Cosmic String Cusps in Real Noise Using Deep Learning

Abstract

Cosmic strings are topological defects that may have formed in the early universe and could produce bursts of gravitational waves through cusp events. Detecting such signals is particularly challenging due to the presence of transient non-astrophysical artifacts—known as glitches—in gravitational-wave detector data. In this work, we develop a deep learning-based classifier designed to distinguish cosmic string cusp signals from common transient noise types, such as blips, using raw, whitened 1D time-series data extracted from real detector noise. Unlike previous approaches that rely on simulated or idealized noise environments, our method is trained and tested entirely on real noise, making it more applicable to real-world search pipelines. Using a dataset of 50,000 labeled 2-second samples, our model achieves a classification accuracy of 84.8% , recall 78.7% and false-positive rate 9.1% on unseen data. This demonstrates the feasibility of cusp-glitch discrimination directly in the time domain, without requiring time-frequency representations or synthetic data, and contributes toward robust detection of exotic astrophysical signals in realistic gravitational-wave conditions.