Concat Convolutional Neural Network for Pulsar Candidate Selection

Pulsar searching is essential for the scientific research in the field of physics and astrophysics. With the development of the radio telescope, the exploding volume and growth speed of candidates have brought about several challenges. Therefore, there is an urgent demand for developing an automatic, accurate, and efficient pulsar candidate selection method. To meet this need, this work designed a Concat Convolutional Neural Network (CCNN) to identify the candidates collected from the Five-hundred-meter Aperture Spherical Telescope (FAST) data. The CCNN extracts some 'pulsar-like' patterns from the diagnostic subplots using Convolutional Neural Network (CNN) and combines these CNN features by a concatenate layer. Therefore, the CCNN is an end-to-end learning model without any need for any intermediate labels, which makes CCNN suitable for the online learning pipeline of pulsar candidate selection. Experimental results on FAST data show that the CCNN outperforms the available state-of-the-art models in a similar scenario. In total, it misses only 4 real pulsars out of 326.