Hierarchical Signal Fusion Network for Pulsar Detection with Phase-Correlation and Signal Attentions

The discovery of pulsars is of great importance to human understanding of the universe. Deep learning has exploited to find pulsars based on radio astronomical folded data, which includes time-phase and frequency-phase images and dispersion curve (DM). In this paper, a hierarchical signal fusion network with phase-correlation and signal attentions are proposed. Specially, signal attentions are designed to reinforce the pulse signals of time-phase or frequency-phase images if a pulsar appears. After that, pulse signal can be reinforced in the same phase where pulses exist in both images. Accordingly, the pulsar search network is implemented by hierarchical data fusion. The first layer is performed at the feature level through phase correlation attention. The second layer of data fusion done at the decision level is performed by calculating a mapping function weighted by the values provided by the phase-correlation attention and filtered out by the DM peak features for the final discrimination . The proposed model is validated on the FAST public dataset with a significant improvement in recall and AUC compared to existing single and multimodal deep models with different attentions.