Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer

TL;DR

This paper introduces SEMD, a deep learning model based on Vision Transformers, for rapid and accurate identification of strongly lensed gravitational wave events, addressing computational challenges of traditional methods.

Contribution

The paper presents SEMD, a novel deep learning architecture that efficiently classifies lensed GW events using morphological features, improving speed and robustness over existing techniques.

Findings

SEMD achieves high accuracy in classifying lensed GW events.

The model demonstrates robustness across different detector sensitivities.

Deep learning enables real-time identification of lensed gravitational waves.

Abstract

With the advancement of third-generation gravitational wave detectors, the identification of strongly lensed gravitational wave (GW) events is expected to play an increasingly vital role in cosmology and fundamental physics. However, traditional Bayesian inference methods suffer from combinatorial computational overhead as the number of events grows, making real-time analysis infeasible. To address this, we propose a deep learning model named Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer (SEMD), based on Vision Transformers, which classifies strongly lensed GW events by modeling morphological similarity between time-frequency spectrogram pairs. By integrating Squeeze-and-Excitation attention mechanisms and multilayer perceptrons , SEMD achieves strong feature extraction and discrimination. Trained and evaluated on simulated datasets using Advanced LIGO and Einstein Telescope noise, the model demonstrates robustness and generalization across different detector sensitivities and physical conditions, highlighting the promise of deep learning for rapid identification of strongly lensed GW signals.