A Naive Bayes Classifier for identifying Class II YSOs
dc.contributor.author | Wilson, AJ | |
dc.contributor.author | Lakeland, BS | |
dc.contributor.author | Wilson, TJ | |
dc.contributor.author | Naylor, T | |
dc.date.accessioned | 2023-01-23T09:22:34Z | |
dc.date.issued | 2023-01-30 | |
dc.date.updated | 2023-01-20T17:03:22Z | |
dc.description.abstract | A 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.sponsorship | Science and Technology Facilities Council (STFC) | en_GB |
dc.description.sponsorship | Leverhulme Trust | en_GB |
dc.identifier.citation | Published online 30 January 2023 | en_GB |
dc.identifier.doi | 10.1093/mnras/stad301 | |
dc.identifier.grantnumber | ST/S006117/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/132302 | |
dc.language.iso | en | en_GB |
dc.publisher | Royal Astronomical Society / Oxford University Press | en_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.subject | stars: formation - stars | en_GB |
dc.subject | stars: pre-main-sequence | en_GB |
dc.subject | stars: variables: T Tauri, Herbig Ae/Be | en_GB |
dc.subject | methods: statistical | en_GB |
dc.subject | catalogues | en_GB |
dc.title | A Naive Bayes Classifier for identifying Class II YSOs | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-01-23T09:22:34Z | |
dc.identifier.issn | 1365-2966 | |
dc.description | This is the author accepted manuscript. The final version is available on open access from Oxford University Press via the DOI in this record | en_GB |
dc.identifier.journal | Monthly Notices of the Royal Astronomical Society | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2023-01-18 | |
dcterms.dateSubmitted | 2022-06-01 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2023-01-18 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-01-20T17:03:24Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2023-02-03T13:41:52Z | |
refterms.panel | B | en_GB |
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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.