Identification of Stochastic Gravitational Wave Backgrounds from Cosmic String Using Machine Learning

TL;DR

This paper demonstrates that machine learning techniques can effectively identify stochastic gravitational wave backgrounds from cosmic strings, especially when combining data from multiple space-based detectors, with high accuracy even in noisy conditions.

Contribution

It introduces a machine learning approach for detecting SGWB from cosmic strings and shows that joint detector analysis improves detection performance over individual detectors.

Findings

Joint detection of LISA and Taiji outperforms individual detectors.

Identification accuracy remains above 95% even with low SNR signals.

Method demonstrated on simulated data, promising for future real observations.

Abstract

Cosmic strings play a crucial role in enhancing our understanding of the fundamental structure and evolution of the universe, unifying our knowledge of cosmology, and potentially unveiling new physical laws and phenomena. The advent and operation of space-based detectors provide an important opportunity for detecting stochastic gravitational wave backgrounds (SGWB) generated by cosmic strings. However, the intricate nature of SGWB poses a formidable challenge in distinguishing its signal from the complex noise by some traditional methods. Therefore, we attempt to identify SGWB based on machine learning. Our findings show that the joint detection of LISA and Taiji significantly outperforms individual detectors, and even in the presence of numerous low signal-to-noise ratio(SNR) signals, the identification accuracy remains exceptionally high with 95%. Although our discussion is based solely on simulated data, the relevant methods can provide data-driven analytical capabilities for future observations of SGWB.