Deep learning-based pulsar candidate identification model using a variational autoencoder

As radio telescope technology continues to advance, the discovery of more pulsar candidates is anticipated. The swift and accurate identification of pulsars has emerged as a compelling research topic in recent years, garnering growing attention. Although deep learning has been expansively applied for the rapid identification of pulsar candidates, the significant imbalance between the larger number of non-pulsar signal samples and the fewer pulsar signal samples seriously impacts the performance of deep learning prediction models. To address this, our research proposes a method that combines a Vector Quantized Variational Autoencoder (VQVAE) with a Gated PixelCNN to equalize the number of pulsar and non-pulsar samples during sample generation. Subsequently, we establish a Residual Neural Network (ResNet) model that employs two feature diagnostic plots as input to classify and identify pulsar candidates. Validation on the High Time Resolution Universe (HTRU) dataset yielded a recall and accuracy of 99.60%, and an F1-score of 99.59%. Further evaluation on the Five-hundred-meter Aperture Spherical radio Telescope (FAST) dataset demonstrated a precision of 98.5%, a recall of 97.8%, and an F1-score of 98.1%, further confirming the effectiveness of our proposed model.

Automated summary

As radio telescope technology advances, the identification of pulsars becomes more important. This research proposes a method that combines a Vector Quantized Variational Autoencoder with a Gated PixelCNN to balance the number of pulsar and non-pulsar samples. A Residual Neural Network model is then established to classify and identify pulsar candidates based on two feature diagnostic plots. The proposed model shows high recall, accuracy, and F1-scores, demonstrating its effectiveness.