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Convolutional Transformer for GW Detection by ahmedashrafabouelelaa is a document available to read on EtoBox.

The document presents a novel architecture called Convolutional Transformer (CoT) for gravitational wave detection, which combines convolutional neural networks and self-attention mechanisms to improve accuracy and inference speed. CoT outperforms traditional CNN-based methods by effectively capturing long-term dependencies in signals and leveraging spectral self-attention for better feature extraction. Experimental results demonstrate that CoT achieves superior performance, with a 0.6% higher AUC/F1 score

Author

ahmedashrafabouelelaa

Language

EN