Enhancing Pulsar Candidate Identification with Self-tuning Pseudolabeling Semisupervised Learning

Abstract

In the field of astronomy, machine-learning technologies are becoming increasingly crucial for identifying radio pulsars. However, the process of acquiring labeled data, which is both time-consuming and potentially biased, poses a significant limitation to current methodologies. In response to these challenges, this study proposes and validates a self-tuning pseudolabeling semisupervised learning approach. This approach synthesizes a vast unlabeled data set with a considerably smaller set of labeled data, markedly enhancing classifier performance and effectuating a transition from traditional fully supervised learning methods to more efficient radio pulsar detection strategies. Our experimental outcomes demonstrate that even with a training set comprised of only 100 labeled pulsar candidates, this method can attain a recall rate of 92.35% and an F1 score of 93.89%. When the number of labeled examples is increased to 800, we observe a further improvement in performance, with the recall rate rising to 97.50% and the F1 score reaching 97.16%. The utility of the semisupervised learning approach is evident even with minimal labeled data, which is a common scenario in the search for pulsars, including in environments like globular clusters. What stands out is the method’s capacity to detect pulsar candidates effectively with only a limited number of labeled examples. This emphasizes the robust potential of our approach to facilitate early-stage pulsar surveys and highlights its capability to yield substantial results even when labeled data are in short supply.