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An all-sky support vector machine selection of WISE YSO candidates

Marton, G. and Tóth, L. V. and Paladini, R. and Kun, M. and Zahorecz, S. and McGehee, P. and Kiss, Cs. (2016) An all-sky support vector machine selection of WISE YSO candidates. Monthly Notices of the Royal Astronomical Society, 458 (4). pp. 3479-3488. ISSN 0035-8711.

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We explored the AllWISE catalogue of the Wide-field Infrared Survey Explorer mission and identified Young Stellar Object candidates. Reliable 2MASS and WISE photometric data combined with Planck dust opacity values were used to build our dataset and to find the best classification scheme. A sophisticated statistical method, the Support Vector Machine (SVM) is used to analyse the multi-dimensional data space and to remove source types identified as contaminants (extragalactic sources, main sequence stars, evolved stars and sources related to the interstellar medium). Objects listed in the SIMBAD database are used to identify the already known sources and to train our method. A new all-sky selection of 133,980 Class I/II YSO candidates is presented. The estimated contamination was found to be well below 1% based on comparison with our SIMBAD training set. We also compare our results to that of existing methods and catalogues. The SVM selection process successfully identified >90% of the Class I/II YSOs based on comparison with photometric and spectroscopic YSO catalogues. Our conclusion is that by using the SVM, our classification is able to identify more known YSOs of the training sample than other methods based on colour-colour and magnitude-colour selection. The distribution of the YSO candidates well correlates with that of the Planck Galactic Cold Clumps in the Taurus–Auriga–Perseus–California region.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Paladini, R.0000-0002-5158-243X
McGehee, P.0000-0003-0948-6716
Additional Information:© 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2016 February 18. Received 2016 February 9; in original form 2015 July 29. First published online February 22, 2016. We thank our anonymous referee for all the useful comments that helped us to improve the manuscript. This publication makes use of data products from the WISE, which is a joint project of the University of California, Los Angeles and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. This research has made use of the SIMBAD data base, operated at CDS, Strasbourg, France. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France. This research was supported by the OTKA grants NN 111016 and K101393.
Group:Infrared Processing and Analysis Center (IPAC)
Funding AgencyGrant Number
Hungarian Scientific Research Fund (OTKA)NN 111016
Hungarian Scientific Research Fund (OTKA)K101393
Subject Keywords:methods: data analysis methods: statistical infrared: general infrared: stars stars: protostars stars: pre-main-sequence astronomical data bases:wise
Issue or Number:4
Record Number:CaltechAUTHORS:20160610-081843327
Persistent URL:
Official Citation:G. Marton, L. V. Tóth, R. Paladini, M. Kun, S. Zahorecz, P. McGehee, and Cs. Kiss An all-sky support vector machine selection of WISE YSO candidates MNRAS (June 01, 2016) Vol. 458 3479-3488 doi:10.1093/mnras/stw398 first published online February 22, 2016
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:67811
Deposited By: Ruth Sustaita
Deposited On:10 Jun 2016 16:00
Last Modified:19 May 2020 22:29

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