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dc.contributor.authorNigri, Eduardo
dc.contributor.authorArandelovic, Ognjen
dc.date.accessioned2017-05-03T08:30:12Z
dc.date.available2017-05-03T08:30:12Z
dc.date.issued2017-06-06
dc.identifier.citationNigri , E & Arandelovic , O 2017 , Light curve analysis from Kepler spacecraft collected data . in International Conference on Multimedia Retrieval, Bucharest, Romania — June 06 - 09, 2017 . ACM , New York , pp. 93-98 , ACM International Conference on Multimedia Retrieval (ICMR 2017) , Bucharest , Romania , 6/06/17 . https://doi.org/10.1145/3078971.3080544en
dc.identifier.citationconferenceen
dc.identifier.isbn9781450347013
dc.identifier.otherPURE: 249892370
dc.identifier.otherPURE UUID: 9bff3456-40f8-4aa1-8efd-2f089c46d86b
dc.identifier.otherScopus: 85021807271
dc.identifier.otherWOS: 000610413000015
dc.identifier.urihttp://hdl.handle.net/10023/10698
dc.descriptionThe authors would like to thank CNPq-Brazil and the University of St Andrews for their kind support.en
dc.description.abstractAlthough scarce, previous work on the application of machine learning and data mining techniques on large corpora of astronomical data has produced promising results. For example, on the task of detecting so-called Kepler objects of interest (KOIs), a range of different ‘off the shelf’ classifiers has demonstrated outstanding performance. These rather preliminary research efforts motivate further exploration of this data domain. In the present work we focus on the analysis of threshold crossing events (TCEs) extracted from photometric data acquired by the Kepler spacecraft. We show that the task of classifying TCEs as being erected by actual planetary transits as opposed to confounding astrophysical phenomena is significantly more challenging than that of KOI detection, with different classifiers exhibiting vastly different performances. Nevertheless,the best performing classifier type, the random forest, achieved excellent accuracy, correctly predicting in approximately 96% of the cases. Our results and analysis should illuminate further efforts into the development of more sophisticated, automatic techniques, and encourage additional work in the area.
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofInternational Conference on Multimedia Retrieval, Bucharest, Romania — June 06 - 09, 2017en
dc.rights© 2017, the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at dl.acm.org / https://doi.org/10.1145/3078971.3080544en
dc.subjectAstronomyen
dc.subjectBig Dataen
dc.subjectPhotometryen
dc.subjectSpaceen
dc.subjectPattern recognitionen
dc.subjectRandom forestsen
dc.subjectSupport vector machineen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQB Astronomyen
dc.subjectQC Physicsen
dc.subject3rd-DASen
dc.subject.lccQA75en
dc.subject.lccQBen
dc.subject.lccQCen
dc.titleLight curve analysis from Kepler spacecraft collected dataen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1145/3078971.3080544


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