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 (GW) detectors, the identification of strongly lensed 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 prescreening classifier, 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 astrophysically motivated simulated datasets incorporating Advanced LIGO and Einstein Telescope noise, SEMD demonstrates robust discrimination and generalization across detector sensitivities and source parameters. By reformulating lens identification as a morphology-based similarity test on paired time–frequency representations of GW signals, SEMD functions as a fast prescreen that prioritizes candidate pairs for subsequent Bayesian follow-up rather than replacing full joint parameter estimation; this substantially reduces the set of events requiring costly posterior reconstruction and thereby enables timely, resource-efficient Bayesian and subsequent multimessenger analyses.