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Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet stars

Morello, Giuseppe and Morris, P. W. and Van Dyk, S. D. and Marston, A. P. and Mauerhan, J. C. (2018) Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet stars. Monthly Notices of the Royal Astronomical Society, 473 (2). pp. 2565-2574. ISSN 0035-8711. http://resolver.caltech.edu/CaltechAUTHORS:20180216-070334093

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Abstract

We have investigated and applied machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet (WR) candidates. Objects taken from the Spitzer Galactic Legacy Infrared Midplane Survey Extraordinaire (GLIMPSE) catalogue of the infrared objects in the Galactic plane can be classified into different stellar populations based on the colours inferred from their broad-band photometric magnitudes [J, H and Ks from 2 Micron All Sky Survey (2MASS), and the four Spitzer/IRAC bands]. The algorithms tested in this pilot study are variants of the k-nearest neighbours approach, which is ideal for exploratory studies of classification problems where interrelations between variables and classes are complicated. The aims of this study are (1) to provide an automated tool to select reliable WR candidates and potentially other classes of objects, (2) to measure the efficiency of infrared colour selection at performing these tasks and (3) to lay the groundwork for statistically inferring the total number of WR stars in our Galaxy. We report the performance results obtained over a set of known objects and selected candidates for which we have carried out follow-up spectroscopic observations, and confirm the discovery of four new WR stars.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/stx2474DOIArticle
https://academic.oup.com/mnras/article/473/2/2565/4243606PublisherArticle
https://arxiv.org/abs/1712.01409arXivDiscussion Paper
ORCID:
AuthorORCID
Van Dyk, S. D.0000-0001-9038-9950
Additional Information:© 2018 Published by Oxford University Press. Accepted 2017 September 21. Received 2017 August 3; in original form 2016 November 12. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research has made use of the SIMBAD data base, operated at CDS, Strasbourg, France. GM was supported by the IPAC Visiting Graduate Student Research Program.
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
NASA/JPL/CaltechUNSPECIFIED
IPAC Visiting Graduate Student Research ProgramUNSPECIFIED
Subject Keywords:methods: observational, methods: statistical, stars: evolution, stars: massive, stars: Wolf–Rayet, infrared: stars
Record Number:CaltechAUTHORS:20180216-070334093
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180216-070334093
Official Citation:Giuseppe Morello, P. W. Morris, S. D. Van Dyk, A. P. Marston, J. C. Mauerhan; Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet stars, Monthly Notices of the Royal Astronomical Society, Volume 473, Issue 2, 11 January 2018, Pages 2565–2574, https://doi.org/10.1093/mnras/stx2474
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84858
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:20 Feb 2018 21:01
Last Modified:20 Feb 2018 21:01

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