Detection of Radio Pulsars in Single-pulse Searches Within and Across Surveys

Abstract

Pulsar detection using machine learning is a challenging problem as it involves extreme class imbalance and strong prioritization of high Recall. This paper is focused on automatic detection of astrophysical pulses in single-pulse searches, both within and across surveys. We use the output from the first stage of our previously developed two-stage Single-Pulse Event Group IDentification approach and focus on the second stage (i.e., classification of pulse candidates). Specifically, for the first time in time-domain single-pulse searches we (1) use boosting and deep learning algorithms for within-survey classification and (2) investigate cross-survey classification by using two transfer learning methods, trAdaBoost (instance-based) and fine-tuning (parameter-based). Our experimental results are based on two benchmark data sets, Green Bank Telescope Drift-scan (GBTDrift) and Pulsar Arecibo L-band Feed Array (PALFA)-extended, created from the GBTDrift survey and the PALFA survey. The main findings include: (1) Due to the emphasis on high Recall, F4 measure is more appropriate performance indicator than the balanced F1 measure. (2) For the GBTDrift benchmark, AdaBoost outperformed the other models with 98.5% Recall and F4 = 0.942. For the PALFA-extended benchmark, RandomForest performed the best, with 91.6% Recall and F4 = 0.890. (3) Models performance degraded significantly when they were used for pulsar classification across surveys. (4) Transfer learning improved cross-survey classification significantly when training data in the target data set were limited. When the GBTDrift benchmark was used as the target data set, fine-tuned SPEGnet models had the highest F4 measure, while trAdaBoost models had the highest F4 measure when PALFA-extended benchmark was used as the target data set. (5) Pulse classification was affected not only by the between-class imbalance (i.e., pulsar versus non-pulsar), but also by the within-class imbalance, which was more prominent in the case of the PALFA-extended benchmark due to the lack of labeled low signal-to-noise ratio pulsars.