Detecting continuous gravitational waves is challenging due to the high computational cost of template-based searches across large parameter spaces, particularly for all-sky searches. Machine learning offers a promising solution to perform these searches with reasonable computational resources. In this study, we trained an attention U-Net, a convolutional neural network, on $\approx 10.67\mathrm{days}$ simulated data with Gaussian noise for all-sky searches at different frequencies within the 20–1000 Hz band. Our model trained at 20 Hz achieves the best sensitivity, with a 90% detection efficiency sensitivity depth ${\mathcal{D}}^{90\%}=29.97\pm 0.24{\mathrm{Hz}}^{-1/2}$ with 1% false alarm rate per 50 mHz, while the model trained on the entire 20–1000 Hz band yields ${\mathcal{D}}^{90\%}=18.63\pm 0.24{\mathrm{Hz}}^{-1/2}$. The sensitivities achieved are comparable to state-of-the-art results using deep learning approaches, with less than 50% of the training time and data. We find that sensitivity scales as $T^{0.28\pm 0.01}$ with total observation time for the attention U-Net trained at 20 Hz, similar to semicoherent search methods. The neural network demonstrates robustness on datasets with time gaps, with sensitivity dependence on duty factor analyzed. We also investigated the sensitivity dependence of the trained attention U-Net models on sky location. Our findings show that attention U-Net is a scalable and effective approach for all-sky continuous gravitational waves searches.

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• Gravitational wave detection
• Deep learning