Search for Gravitational Wave Probes - A Self-Supervised Learning for Pulsars Based on Signal Contexts

The recent successful detection of gravitational waves (GWs) at nanohertz based on pulsar timing arrays has underscored the growing significance of searching for new pulsars, which serve as valuable probes for GWs. However, one of the challenges in this endeavor is the lack of labeled data, which can lead to overfitting and poor generalization in supervised deep neural networks. In this paper, we propose a self-supervised pretext task based on signal con-texts to obtain discriminative radio signal representation. Specially, signal attentions are designed to enhance pulse signals within time-phase or frequency-phase images whenever a pulsar is detected. To validate our proposed model, we conducted experiments using the FAST public dataset with a significant improvement in recall and AUC compared to existing single and multimodal deep models with different attentions. As a result, we searched more than 30 new pulsars.