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Machine-learning approaches to select Wolf-Rayet candidates

Published online by Cambridge University Press:  28 July 2017

A. P. Marston ,
G. Morello ,
P. Morris ,
S. Van Dyk  and
J. Mauerhan
A. P. Marston
Affiliation:
ESA, STScI, 3700 San Martin Drive, Baltimore, MD 21218, USA email: [email protected]
G. Morello
Affiliation:
IPAC, Caltech, 1200 E. California Blvd, Pasadena, CA 91125, USA Dept. of Physics and Astronomy, UCL, Gower Street, WC1E 6BT, UK
P. Morris
Affiliation:
IPAC, Caltech, 1200 E. California Blvd, Pasadena, CA 91125, USA
S. Van Dyk
Affiliation:
IPAC, Caltech, 1200 E. California Blvd, Pasadena, CA 91125, USA
J. Mauerhan
Affiliation:
Dept. of Astronomy, University of California, Berkeley, CA, USA
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Abstract

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The WR stellar population can be distinguished, at least partially, from other stellar populations by broad-band IR colour selection. We present the use of a machine learning classifier to quantitatively improve the selection of Galactic Wolf-Rayet (WR) candidates. These methods are used to separate the other stellar populations which have similar IR colours. We show the results of the classifications obtained by using the 2MASS J, H and K photometric bands, and the Spitzer/IRAC bands at 3.6, 4.5, 5.8 and 8.0μm. The k-Nearest Neighbour method has been used to select Galactic WR candidates for observational follow-up. A few candidates have been spectroscopically observed. Preliminary observations suggest that a detection rate of 50% can easily be achieved.

Keywords

Infrared surveysWolf-Rayet stars
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S329: The Lives and Death-Throes of Massive Stars , November 2016 , pp. 422
Copyright
Copyright © International Astronomical Union 2017 

References

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Mauerhan, J. C., Van Dyk, S. D., & Morris, P. W., 2011, Astronomical Journal, 142, 40 Google Scholar
Morello, G., Morris, P. W., Van Dyk, S. D., Marston, A. P., & Mauerhan, J. C. 2017, MNRAS, in reviewGoogle Scholar