Improving pulsar search efficiency in next-generation pulsar surveys with artificial intelligence

Abstract

Pulsar searching with next-generation radio telescopes requires efficiently sifting through millions of candidates generated by search pipelines to identify the most promising ones. This challenge has motivated the utilization of Artificial Intelligence (AI)-based tools. In this work, we explore an optimized pulsar search pipeline that utilizes deep learning to sift ``snapshot'' candidates generated by folding de-dispersed time series data. This approach significantly accelerates the search process by reducing the time spent on the folding step. We also developed a script to generate simulated pulsars for benchmarking and model fine-tuning. The benchmark analysis used the NGC 5904 globular cluster data and simulated pulsar data, showing that our pipeline reduces candidate folding time by a factor of $\sim$10 and achieves 100% recall by recovering all known detectable pulsars in the restricted parameter space. We also tested the speed-up using data of known pulsars from a single observation in the Southern-sky MWA Rapid Two-metre (SMART) survey, achieving a conservatively estimated speed-up factor of 60 in the folding step over a large parameter space. We tested the model's ability to classify pulsar candidates using real data collected from the FAST, GBT, MWA, Arecibo, and Parkes, demonstrating that our method can be generalized to different telescopes. The results show that the optimized pipeline identifies pulsars with an accuracy of 0.983 and a recall of 0.9844 on the real dataset. This approach can be used to improve the processing efficiency for the SMART and is also relevant for future SKA pulsar surveys.