Detection of Lensed Gravitational Waves in the Millihertz Band Using Frequency-Domain Lensing Feature Extraction Network

TL;DR

This paper presents DCL-xLSTM, a novel neural network that efficiently detects lensed gravitational wave signals in the millihertz band, outperforming traditional methods in accuracy and robustness.

Contribution

Introduction of DCL-xLSTM, a new neural network architecture designed for efficient and accurate detection of lensed gravitational waves in space-based detectors.

Findings

Achieves AUC > 0.99 in detection tasks.

Maintains TPR > 98% at FPR < 1%.

Robust against variations in SNR, lens type, and lens mass.

Abstract

The space-based gravitational wave (GW) detectors are expected to observe lensed GW events, offering new opportunities for cosmology and fundamental physics.Across the millihertz band, lensing effects transition from the wave-optics regime at lower frequencies to the geometric-optics approximation at higher frequencies.Although traditional GW identification methods, such as matched filtering, are well established and effective, the intense computational resources required motivate the search for more efficient alternatives to accelerate candidate event screening. To address this bottleneck, we introduce a Dual-Channel Lensing feature extraction eXtended Long Short-Term Memory Network (DCL-xLSTM). Unlike conventional recurrent architectures, DCL-xLSTM uses a matrix-valued memory structure and a memory-mixing mechanism to effectively capture amplitude patterns that span the entire millihertz frequency band. Trained on data generated by Point Mass (PM) and Singular Isothermal Sphere (SIS) models accounting for the transition from wave-optics to geometric-optics, the proposed method achieves an area under the curve (AUC) exceeding 0.99, maintaining a true positive rate (TPR) above $98\%$ at a false positive rate (FPR) below $1\%$.The network is robust against variations in signal-to-noise ratio, lens type, and lens mass, establishing its viability as a high-efficiency tool for future space-based GW detection.