Enhancing Single Pulse Detection: A Novel Search Model Addresses Sample Imbalance and Boosts Recognition Accuracy

With the rapid expansion of pulsar survey data driven by advanced radio telescopes such as FAST, automated detection methods have become crucial for the efficient and accurate identification of single-pulse signals. A key challenge in this task is the extreme class imbalance between genuine pulsar pulses and radio frequency interference (RFI), which significantly hampers classifier performance—particularly in low signal-to-noise ratio (S/N) environments. To address this issue and improve detection accuracy, we propose Pulsar-WRecon, a Wasserstein GAN with Gradient Penalty (WGAN-GP)-based framework designed to generate realistic single-pulse profiles. The synthetic samples generated by Pulsar-WRecon are used to augment training data and alleviate class imbalance. Building upon the enhanced dataset, Convolutional Kolmogorov–Arnold Network (CKAN) is further introduced as a novel hybrid model that integrates convolutional layers with KAN-based functional decomposition to better capture complex patterns in pulse signals. On the three-channel pulsar images from the HTRU1 dataset, our method achieves a recall of 97.5% and a precision of 98.5%. On the DM time series image dataset, FAST-DATASET, it achieves a recall of 93.2% and a precision of 92.5%. These results validate that combining generative data augmentation with an improved model architecture can effectively enhance the precision of single-pulse detection in large-scale pulsar surveys, especially in challenging, real-world conditions.