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

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.

11 pages, 5 figures, comments are welcome

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Cited by in corpus (1)

• Mock Catalogs of Strongly Lensed Gravitational Waves via A Halo Model Approach with Ground-based Detectors