Space-based gravitational wave signal detection and extraction with deep neural network

TL;DR

This paper introduces a deep neural network approach for detecting and extracting gravitational wave signals from space-based detectors, offering high accuracy and efficiency over traditional methods.

Contribution

A multi-stage self-attention deep neural network is developed for gravitational wave detection, reducing computational costs and improving accuracy compared to matched filtering.

Findings

Detection rate exceeds 99% at SNR 50

False alarm rate is 1%

Achieves at least 95% similarity with target signals

Abstract

Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.