Pulsar Candidate Identification Using Semi-Supervised Generative Adversarial Networks

Abstract

Machine learning methods are increasingly helping astronomers identify new radio pulsars. However, they require a large amount of labelled data, which is time consuming to produce and biased. Here we describe a Semi-Supervised Generative Adversarial Network (SGAN) which achieves better classification performance than the standard supervised algorithms using majority unlabelled datasets. We achieved an accuracy and mean F-Score of 94.9% trained on only 100 labelled candidates and 5000 unlabelled candidates compared to our standard supervised baseline which scored at 81.1% and 82.7% respectively. Our final model trained on a much larger labelled dataset achieved an accuracy and mean F-score value of 99.2% and a recall rate of 99.7%. This technique allows for high quality classification during the early stages of pulsar surveys on new instruments when limited labelled data is available. We open-source our work along with a new pulsar-candidate dataset produced from the High Time Resolution Universe - South Low Latitude Survey. This dataset has the largest number of pulsar detections of any public dataset and we hope it will be a valuable tool for benchmarking future machine learning models.

added coauthors and one extra plot, main results unchanged, accepted by MNRAS journal

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• Missing for 20 years: MeerKAT re-detects the elusive binary pulsar M30B
• The Southern-sky MWA Rapid Two-metre (SMART) pulsar survey -- II. Survey status, pulsar census, and first pulsar discoveries
• Morphological Classification of Radio Galaxies with wGAN-supported Augmentation
• Analyzing the Galactic pulsar distribution with machine learning
• Classification of the \emph{Fermi}-LAT Blazar Candidates of Uncertain type using eXtreme Gradient Boosting
• Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation