A novel single-pulse search approach to detection of dispersed radio pulses using clustering and supervised machine learning (1807.07164v2)

Abstract: We present a novel two-stage approach which combines unsupervised and supervised machine learning to automatically identify and classify single pulses in radio pulsar search data. In the first stage, we identify astrophysical pulse candidates in the data, which were derived from the Pulsar Arecibo L-Band Feed Array (PALFA) survey and contain 47,042 independent beams, as trial single-pulse event groups (SPEGs) by clustering single-pulse events and merging clusters that fall within the expected DM and time span of astrophysical pulses. We also present a new peak scoring algorithm, to identify astrophysical peaks in S/N versus DM curves. Furthermore, we group SPEGs detected at a consistent DM for they were likely emitted by the same source. In the second stage, we create a fully labelled benchmark data set by selecting a subset of data with SPEGs identified (using stage 1 procedures), their features extracted and individual SPEGs manually labelled, and then train classifiers using supervised machine learning. Next, using the best trained classifier, we automatically classify unlabelled SPEGs identified in the full data set. To aid the examination of dim SPEGs, we develop an algorithm that searches for an underlying periodicity among grouped SPEGs. The results showed that RandomForest with SMOTE treatment was the best learner, with a recall of 95.6% and a false positive rate of 2.0%. In total, besides all 60 known pulsars from the benchmark data set, the model found 32 additional (i.e., not included in the benchmark data set) known pulsars, and several potential discoveries.