(Q59767015)

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Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys

scholarly article by N Schanche et al published 22 November 2018 in Monthly Notices of the Royal Astronomical Society

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Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys (English)

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D Queloz

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Stéphane Udry

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S Udry

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François Bouchy

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F Bouchy

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Amaury Triaud

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A H M J Triaud

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Andrew Collier Cameron

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A Collier Cameron

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Francesca Faedi

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David R. Anderson

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D R Anderson

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Enric Palle

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E Palle

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David J. A. Brown

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Khalid Alsubai

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Pierre F. L. Maxted

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P F L Maxted

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Barry Smalley

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B Smalley

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Don Pollacco

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D L Pollacco

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N Schanche

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G HÃl’brard

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J M Almenara

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D J Armstrong

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S C C Barros

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P Boumis

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K Hay

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L Hebb

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F Kiefer

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L Mancini

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R West

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P J Wheatley

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L Nielsen

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English

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22 November 2018

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Monthly Notices of the Royal Astronomical Society

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stated in

Astrophysics Data System

retrieved

15 December 2022

reference URL

https://ui.adsabs.harvard.edu/abs/2001MachL..45....5B/citations

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DOI

10.1093/MNRAS/STY3146

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(Q59767015)

Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys

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