Light curve analysis from Kepler spacecraft collected data

Eduardo Nigri, Ognjen Arandelovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationInternational Conference on Multimedia Retrieval, Bucharest, Romania — June 06 - 09, 2017
Place of PublicationNew York
PublisherACM
Pages93-98
ISBN (Print)9781450347013
DOIs
Publication statusPublished - 6 Jun 2017
EventACM International Conference on Multimedia Retrieval (ICMR 2017) - Bucharest, Romania
Duration: 6 Jun 20179 Jun 2017
http://www.icmr2017.ro/index.php

Conference

ConferenceACM International Conference on Multimedia Retrieval (ICMR 2017)
Abbreviated titleICMR 2017
Country/TerritoryRomania
CityBucharest
Period6/06/179/06/17
Internet address

Keywords

  • Astronomy
  • Big Data
  • Photometry
  • Space
  • Pattern recognition
  • Random forests
  • Support vector machine

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