KOI-3509 , the SIMBAD biblio

KOI-3509 , the SIMBAD biblio (11 results) C.D.S. - SIMBAD4 rel 1.8 - 2024.04.19CEST23:54:02

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Bibcode/DOI Score in Title|Abstract|
Keywords 		in a table in teXt, Caption, ... Nb occurence Nb objects in ref Citations
(from ADS) 		Title First 3 Authors
2013MNRAS.429.2001H viz 16       D               1 140 33 150 new transiting planet candidates from Kepler Q1-Q6 data. HUANG X., BAKOS G.A. and HARTMAN J.D.
2014AJ....147..119C viz 16       D               1 8010 91 Contamination in the Kepler field. Identification of 685 KOIs as false positives via ephemeris matching based on Q1-Q12 data. COUGHLIN J.L., THOMPSON S.E., BRYSON S.T., et al.
2015ApJ...801....3M viz 16       D               1 3357 109 Photometric amplitude distribution of stellar rotation of KOIs–Indication for spin-orbit alignment of cool stars and high obliquity for hot stars. MAZEH T., PERETS H.B., McQUILLAN A., et al.
2015ApJS..217...16R viz 16       D               1 8625 149 Planetary candidates observed by Kepler. V. Planet sample from Q1-Q12 (36 months). ROWE J.F., COUGHLIN J.L., ANTOCI V., et al.
2015ApJ...807..170H viz 16       D               1 2117 10 Time variation of Kepler transits induced by stellar Spots–A way to distinguish between prograde and retrograde motion. II. Application to KOIs. HOLCZER T., SHPORER A., MAZEH T., et al.
2016ApJ...822...86M viz 16       D               1 6130 337 False positive probabilities for all Kepler objects of interest: 1284 newly validated planets and 428 likely false positives. MORTON T.D., BRYSON S.T., COUGHLIN J.L., et al.
2016ApJS..225....9H viz 16       D               1 2132 124 Transit timing observations from Kepler. IX. Catalog of the full long-cadence data set. HOLCZER T., MAZEH T., NACHMANI G., et al.
2017MNRAS.465.2634A viz 16       D               1 5400 21 Transit shapes and self-organizing maps as a tool for ranking planetary candidates: application to Kepler and K2. ARMSTRONG D.J., POLLACCO D. and SANTERNE A.
2017MNRAS.469.4578H viz 16       D               1 8666 48 Deep learning classification in asteroseismology. HON M., STELLO D. and YU J.
2018MNRAS.476.3233H viz 16       D               1 15009 17 Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data. HON M., STELLO D. and YU J.
2018ApJS..236...42Y viz 16       D               1 16097 158 Asteroseismology of 16,000 Kepler red giants: global oscillation parameters, masses, and radii. YU J., HUBER D., BEDDING T.R., et al.

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