Light curve analysis from Kepler spacecraft collected data
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Although 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.
Nigri , 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 , ACM International Conference on Multimedia Retrieval (ICMR 2017) , Bucharest , Romania , 6-9 June . DOI: 10.1145/3078971.3080544conference
International Conference on Multimedia Retrieval, Bucharest, Romania — June 06 - 09, 2017
© 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/10.1145/3078971.3080544
The authors would like to thank CNPq-Brazil and the University of St Andrews for their kind support.
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