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2018MNRAS.478.4225A - Mon. Not. R. Astron. Soc., 478, 4225-4237 (2018/August-2)
Automatic vetting of planet candidates from ground-based surveys: machine learning with NGTS.
ARMSTRONG D.J., GUNTHER M.N., McCORMAC J., SMITH A.M.S., BAYLISS D., BOUCHY F., BURLEIGH M.R., CASEWELL S., EIGMULLER P., GILLEN E., GOAD M.R., HODGKIN S.T., JENKINS J.S., LOUDEN T., METRAILLER L., POLLACCO D., POPPENHAEGER K., QUELOZ D., RAYNARD L., RAUER H., UDRY S., WALKER S.R., WATSON C.A., WEST R.G. and WHEATLEY P.J.
Abstract (from CDS):
Abstract Copyright: © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society
Journal keyword(s): methods: data analysis - methods: statistical - planets and satellites: detection - planets and satellites: general
Simbad objects: 5
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