Show simple item record

dc.contributor.authorWilson, AJ
dc.contributor.authorLakeland, BS
dc.contributor.authorWilson, TJ
dc.contributor.authorNaylor, T
dc.date.accessioned2023-01-23T09:22:34Z
dc.date.issued2023-01-30
dc.date.updated2023-01-20T17:03:22Z
dc.description.abstractA naive Bayes classifier for identifying Class II YSOs has been constructed and applied to a region of the Northern Galactic Plane containing 8 million sources with good quality Gaia EDR3 parallaxes. The classifier uses the five features: Gaia G-band variability, WISE mid-infrared excess, UKIDSS and 2MASS near-infrared excess, IGAPS Hα excess and overluminosity with respect to the main sequence. A list of candidate Class II YSOs is obtained by choosing a posterior threshold appropriate to the task at hand, balancing the competing demands of completeness and purity. At a threshold posterior greater than 0.5 our classifier identifies 6504 candidate Class II YSOs. At this threshold we find a false positive rate around 0.02 per cent and a true positive rate of approximately 87 per cent for identifying Class II YSOs. The ROC curve rises rapidly to almost one with an area under the curve around 0.998 or better, indicating the classifier is efficient at identifying candidate Class II YSOs. Our map of these candidates shows what are potentially three previously undiscovered clusters or associations. When comparing our results to published catalogues from other young star classifiers, we find between one quarter and three quarters of high probability candidates are unique to each classifier, telling us no single classifier is finding all young stars.en_GB
dc.description.sponsorshipScience and Technology Facilities Council (STFC)en_GB
dc.description.sponsorshipLeverhulme Trusten_GB
dc.identifier.citationPublished online 30 January 2023en_GB
dc.identifier.doi10.1093/mnras/stad301
dc.identifier.grantnumberST/S006117/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/132302
dc.language.isoenen_GB
dc.publisherRoyal Astronomical Society / Oxford University Pressen_GB
dc.rights© The Author(s) 2023. Published by Oxford University Press on behalf of The Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en_GB
dc.subjectstars: formation - starsen_GB
dc.subjectstars: pre-main-sequenceen_GB
dc.subjectstars: variables: T Tauri, Herbig Ae/Been_GB
dc.subjectmethods: statisticalen_GB
dc.subjectcataloguesen_GB
dc.titleA Naive Bayes Classifier for identifying Class II YSOsen_GB
dc.typeArticleen_GB
dc.date.available2023-01-23T09:22:34Z
dc.identifier.issn1365-2966
dc.descriptionThis is the author accepted manuscript. The final version is available on open access from Oxford University Press via the DOI in this recorden_GB
dc.identifier.journalMonthly Notices of the Royal Astronomical Societyen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2023-01-18
dcterms.dateSubmitted2022-06-01
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2023-01-18
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-01-20T17:03:24Z
refterms.versionFCDAM
refterms.dateFOA2023-02-03T13:41:52Z
refterms.panelBen_GB


Files in this item

This item appears in the following Collection(s)

Show simple item record

© The Author(s) 2023. Published by Oxford University Press on behalf of The Royal Astronomical Society.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's licence is described as © The Author(s) 2023. Published by Oxford University Press on behalf of The Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.