摘要
As the performance of dedicated facilities has continually improved, large numbers of pulsar candidates are being received, which makes selecting valuable pulsar signals from the candidates challenging. In this paper, we describe the design for a deep convolutional neural network(CNN) with 11 layers for classifying pulsar candidates. Compared to artificially designed features, the CNN chooses the subintegrations plot and sub-bands plot for each candidate as inputs without carrying biases. To address the imbalance problem, a data augmentation method based on synthetic minority samples is proposed according to the characteristics of pulsars. The maximum pulses of pulsar candidates were first translated to the same position, and then new samples were generated by adding up multiple subplots of pulsars. The data augmentation method is simple and effective for obtaining varied and representative samples which keep pulsar characteristics. In experiments on the HTRU 1 dataset, it is shown that this model can achieve recall of 0.962 and precision of 0.963.
As the performance of dedicated facilities has continually improved, large numbers of pulsar candidates are being received, which makes selecting valuable pulsar signals from the candidates challenging. In this paper, we describe the design for a deep convolutional neural network(CNN) with 11 layers for classifying pulsar candidates. Compared to artificially designed features, the CNN chooses the subintegrations plot and sub-bands plot for each candidate as inputs without carrying biases. To address the imbalance problem, a data augmentation method based on synthetic minority samples is proposed according to the characteristics of pulsars. The maximum pulses of pulsar candidates were first translated to the same position, and then new samples were generated by adding up multiple subplots of pulsars. The data augmentation method is simple and effective for obtaining varied and representative samples which keep pulsar characteristics. In experiments on the HTRU 1 dataset, it is shown that this model can achieve recall of 0.962 and precision of 0.963.
