Abstract
ABSTRACT The pulsar detection survey has contributed to the study of celestial evolution by providing scientists with a large amount of observational data. In addition, the amount of data collected by the survey has grown exponentially, and there is a large class imbalance in the corresponding data. In this paper, we design a residual convolutional autoencoder (RCAE) based on the structure of the autoencoder, and combine with logistic regression (LR) to construct a network structure framework suitable for pulsar candidate identification. RCAE is used as the primary model to fit the data distribution of the non-pulsar sample, the process does not need to consider the positive and negative pulsar sample imbalance. LR is used as an auxiliary classification model to test the final results. The experimental results on the HTRU Medlat and PMPS-26k data sets show that the best performance is achieved without the use of data generation and complex enhancement methods.
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