Detection of Lensed Gravitational Waves from dark matter halos with deep learning

TL;DR

This paper presents a deep learning framework that effectively detects and classifies lensed gravitational waves from dark matter halos using multiband observations, significantly improving accuracy over single-detector methods.

Contribution

The study introduces a residual 1D CNN model for end-to-end classification of lensed GWs across multiple observation bands, demonstrating high accuracy with simulated data.

Findings

Achieves 97% accuracy in classifying lensed GWs with multiband data.

Outperforms single-detector models by a large margin.

Maintains high accuracy even at low SNR levels.

Abstract

Lensed gravitational waves (GWs) provide a new window into the study of dark matter substructures, yet the faint interference signatures they produce are buried in detector noise. To address this challenge, we develop a deep learning framework based on a residual one-dimensional convolutional neural network for lensed GW identification under multiband observations. The model directly processes multiband waveforms from binary neutron star systems, covering the early inspiral observed by the DECi-hertz Interferometer Gravitational wave Observatory (DECIGO) and the late inspiral observed by the Einstein Telescope (ET), corresponding approximately to the wave-optics and geometrical-optics regimes, respectively. It enables end-to-end classification of five classes: pure noise, unlensed GWs, and three representative lensed GWs corresponding to singular isothermal sphere (SIS), cored isothermal sphere (CIS), and Navarro-Frenk-White (NFW) profiles. A dataset of 10^6 simulated samples was constructed with signal-to-noise ratios (SNR) ranging from 5 to 100. The deep learning model with multiband observations achieves an accuracy of 97.0% and a macro-averaged F1 score of 0.97, significantly exceeding the single-detector performance, where DECIGO and ET reach 72.8% and 62.3%, respectively. Even in the low-SNR regime (SNR < 20), the model maintains an accuracy above 63%, while in the high-SNR regime (SNR > 80), its accuracy approaches 99.8%. These results demonstrate that multiband GW observations effectively enhance the detection of lensed GWs within complex noise environments, providing a robust and efficient pathway for the automated identification of lensed GWs in future multiband observations.