Pulsar Identification Based on Variational Autoencoder and Residual Network

In modern astronomy, pulsar identification is a vital task to help researchers discovering new pulsars. With the great progress of modern radio telescopes improves, the amount of pulsar data collected increases exponentially, which causes the traditional pulsar identification approaches to be not enough to tackle such a large dataset. At present, many pulsar identification methods achieve promising performance based on deep neural networks. However, those neural-network-based methods still face the sample imbalance problem, which limits their performance. To be specific, the pulsar sample imbalance problem is that only an extremely limited number of real pulsar samples exist in dataset. To alleviate the problem and enhance the pulsar identification performance, we present a novel method under the framework of synergetic learning systems which includes the variational autoencoder and residual network. In this work, the variational autoencoder is used to generate some high-quality pulsar samples for training procedure to mitigate the pulsar sample imbalance problem, and then we present a residual-network-based model to promote pulsar candidate identification performance. Extensive experiments on two pulsar datasets demonstrate that our framework not only alleviates the imbalance problem, but also improves the accuracy of pulsar identification.