EDAT-BBH: An Energy-Modulated Transformer with Dual-Energy Attention Masks for Binary Black Hole Signal Classification

Gravitational-wave (GW) detection has become a significant area of research following the first successful observation by the Laser Interferometer Gravitational-Wave Observatory (LIGO). The detection of signals emerging from binary black hole (BBH) mergers have challenges due to the presence of non-Gaussian and non-stationary noise in observational data. Using traditional matched filtering techniques to detect BBH merging are computationally expensive and may not generalize well to unexpected GW events. As a result, deep learning-based methods have emerged as powerful alternatives for robust GW signal detection. In this study, we propose a novel Transformer-based architecture that introduces energy-aware modulation into the attention mechanism through dual-energy attention masks. In the proposed framework, Q-transform and discrete wavelet transform (DWT) are employed to extract time–frequency energy representations from gravitational-wave signals which are fused into energy masks that dynamically guide the Transformer encoder. In parallel, the raw one-dimensional signal is used directly as input and segmented into temporal patches, which enables the model to leverage both learned representations and physically grounded priors. This proposed architecture allows the model to focus on energy-rich and informative regions of the signal in order to enhance the robustness of the model under realistic noise conditions. Experimental results on BBH datasets embedded in real LIGO noise show that EDAT-BBH outperforms CNN-based and standard Transformer-based approaches, achieving an accuracy of 0.9953, a recall of 0.9950, an F1-score of 0.9953, and an AUC of 0.9999. These findings demonstrate the effectiveness of energy-modulated attention in improving both the interpretability and performance of deep learning models for gravitational-wave signal classification.