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dc.contributor.authorSchanche, N.
dc.contributor.authorCameron, A. Collier
dc.contributor.authorHébrard, G.
dc.contributor.authorNielsen, L.
dc.contributor.authorTriaud, A. H. M. J.
dc.contributor.authorAlmenara, J. M.
dc.contributor.authorAlsubai, K. A.
dc.contributor.authorAnderson, D. R.
dc.contributor.authorArmstrong, D. J.
dc.contributor.authorBarros, S. C. C.
dc.contributor.authorBouchy, F.
dc.contributor.authorBoumis, P.
dc.contributor.authorBrown, D. J. A.
dc.contributor.authorFaedi, F.
dc.contributor.authorHay, K.
dc.contributor.authorHebb, L.
dc.contributor.authorKiefer, F.
dc.contributor.authorMancini, L.
dc.contributor.authorMaxted, P. F. L.
dc.contributor.authorPalle, E.
dc.contributor.authorPollacco, D. L.
dc.contributor.authorQueloz, D.
dc.contributor.authorSmalley, B.
dc.contributor.authorUdry, S.
dc.contributor.authorWest, R.
dc.contributor.authorWheatley, P. J.
dc.date.accessioned2019-01-14T12:30:06Z
dc.date.available2019-01-14T12:30:06Z
dc.date.issued2018-11-22
dc.identifier.citationSchanche , N , Cameron , A C , Hébrard , 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 & Wheatley , P J 2018 , ' Machine-learning approaches to exoplanet transit detection and candidate validation in wide-field ground-based surveys ' , Monthly Notices of the Royal Astronomical Society , vol. 483 , pp. 5534-5547 . https://doi.org/10.1093/mnras/sty3146en
dc.identifier.issn0035-8711
dc.identifier.otherPURE: 257275246
dc.identifier.otherPURE UUID: 746b2cf8-fdaa-4cce-b3a7-b6aaee171947
dc.identifier.otherBibCode: 2018MNRAS.tmp.2997S
dc.identifier.otherORCID: /0000-0002-8863-7828/work/58531367
dc.identifier.otherScopus: 85062298418
dc.identifier.otherWOS: 000462281900091
dc.identifier.urihttp://hdl.handle.net/10023/16858
dc.descriptionACC acknowledges support from STFC consolidated grant ST/R000824/1 and UK Space Agency grant ST/R003203/1.en
dc.description.abstractSince the start of the Wide Angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by human inspection to eliminate false alarms and obvious false positives. The goal of the present paper is to assess the effectiveness of machine learning as a fast, automated, and reliable means of performing the same functions on ground-based wide-field transit-survey data without human intervention. To this end, we have created training and test datasets made up of stellar light curves showing a variety of signal types including planetary transits, eclipsing binaries, variable stars, and non-periodic signals. We use a combination of machine learning methods including Random Forest Classifiers (RFCs) and Convolutional Neural Networks (CNNs) to distinguish between the different types of signals. The final algorithms correctly identify planets in the test data ∼90% of the time, although each method on its own has a significant fraction of false positives. We find that in practice, a combination of different methods offers the best approach to identifying the most promising exoplanet transit candidates in data from WASP, and by extension similar transit surveys.
dc.format.extent14
dc.language.isoeng
dc.relation.ispartofMonthly Notices of the Royal Astronomical Societyen
dc.rightsCopyright © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This work is made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at: https://doi.org/10.1093/mnras/sty3146en
dc.subjectPlanets and satellites: detectionen
dc.subjectMethods: statisticalen
dc.subjectMethods: data analysisen
dc.subjectQB Astronomyen
dc.subject3rd-NDASen
dc.subject.lccQBen
dc.titleMachine-learning approaches to exoplanet transit detection and candidate validation in wide-field ground-based surveysen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.St Andrews Centre for Exoplanet Scienceen
dc.contributor.institutionUniversity of St Andrews.School of Physics and Astronomyen
dc.identifier.doihttps://doi.org/10.1093/mnras/sty3146
dc.description.statusPeer revieweden
dc.identifier.urlhttp://adsabs.harvard.edu/abs/2018MNRAS.tmp.2997Sen


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