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2019MNRAS.483.5534S - Mon. Not. R. Astron. Soc., 483, 5534-5547 (2019/March-2)
Machine-learning approaches to exoplanet transit detection and candidate validation in wide-field ground-based surveys.
SCHANCHE N., CAMERON A.C., HEBRARD G., NIELSEN L., TRIAUD A.H.M.J., ALMENARA J.M., ALSUBAI K.A., ANDERSON D.R., ARMSTRONG D.J., BARROS S.C.C., BOUCHY F., BOUMIS P., BROWN D.J.A., FAEDI F., HAY K., HEBB L., KIEFER F., MANCINI L., MAXTED P.F.L., PALLE E., POLLACCO D.L., QUELOZ D., SMALLEY B., UDRY S., WEST R. 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
CDS comments: SW HHMM+DD objects are not in Simbad.
Simbad objects: 6
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