Ensemble candidate classification for the LOTAAS pulsar survey

Chia Min Tan, ROBERT LYON, Benjamin Stappers, Sally Cooper, Jason Hessels, Vald Kondratiev, Daniele Michilli, Sotiris Sanidas

Research output: Contribution to journalArticle (journal)peer-review

28 Citations (Scopus)
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One of the biggest challenges arising from modern large-scale pulsar surveys is the number of candidates generated. Here, we implemented several improvements to the machine learning (ML) classifier previously used by the LOFAR Tied-Array All-Sky Survey (LOTAAS) to look for new pulsars via filtering the candidates obtained during periodicity searches. To assist the ML algorithm, we have introduced new features which capture the frequency and time evolution of the signal and improved the signal-to-noise calculation accounting for broad profiles. We enhanced the ML classifier by including a third class characterizing RFI instances, allowing candidates arising from RFI to be isolated, reducing the false positive return rate. We also introduced a new training data set used by the ML algorithm that includes a large sample of pulsars misclassified by the previous classifier. Lastly, we developed an ensemble classifier comprised of five different Decision Trees. Taken together these updates improve the pulsar recall rate by 2.5 per cent, while also improving the ability to identify pulsars with wide pulse profiles, often misclassified by the previous classifier. The new ensemble classifier is also able to reduce the percentage of false positive candidates identified from each LOTAAS pointing from 2.5 per cent (∼500 candidates) to 1.1 per cent (∼220 candidates).
Original languageEnglish
Pages (from-to)4571-4583
Number of pages13
JournalMonthly Notices of the Royal Astronomical Society
Issue number4
Early online date29 Nov 2017
Publication statusPublished - 1 Mar 2018


  • Methods: Data analysis
  • Methods: Statistical
  • Pulsars: General


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