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Light curve analysis from Kepler spacecraft collected data

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Nigri_ICMR2017_Light_curve_analysis_AAM.pdf (1.265Mb)
Date
06/06/2017
Author
Nigri, Eduardo
Arandelovic, Ognjen
Keywords
Astronomy
Big Data
Photometry
Space
Pattern recognition
Random forests
Support vector machine
QA75 Electronic computers. Computer science
QB Astronomy
QC Physics
3rd-DAS
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Abstract
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.
Citation
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 , pp. 93-98 , ACM International Conference on Multimedia Retrieval (ICMR 2017) , Bucharest , Romania , 6/06/17 . https://doi.org/10.1145/3078971.3080544
 
conference
 
Publication
International Conference on Multimedia Retrieval, Bucharest, Romania — June 06 - 09, 2017
DOI
https://doi.org/10.1145/3078971.3080544
Type
Conference item
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/10.1145/3078971.3080544
Description
The authors would like to thank CNPq-Brazil and the University of St Andrews for their kind support.
Collections
  • Computer Science Research
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/10698

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