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Ensemble candidate classification for the LOTAAS pulsar survey

Tan, CM, Lyon, RJ, Stappers, BW, Cooper, S, Hessels, JWT, Kondratiev, VI, Michilli, D and Sanidas, S (2017) Ensemble candidate classification for the LOTAAS pulsar survey. Monthly Notices of the Royal Astronomical Society, 474 (4). pp. 4571-4583. ISSN 0035-8711

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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).

Item Type: Article
Additional Information: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2017 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Uncontrolled Keywords: 0201 Astronomical and Space Sciences; Astronomy & Astrophysics
Subjects: Q Science > QB Astronomy
Divisions: Computer Science & Mathematics
Publisher: Oxford University Press (OUP)
SWORD Depositor: A Symplectic
Date Deposited: 18 Sep 2023 08:35
Last Modified: 18 Sep 2023 08:45
DOI or ID number: 10.1093/mnras/stx3047
URI: https://researchonline.ljmu.ac.uk/id/eprint/21464
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