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Detecting Variability in Massive Astronomical Time-series Data. III. Variable Candidates in the SuperWASP DR1 Found by Multiple Clustering Algorithms and a Consensus Clustering Method

Shin, Min-Su; Chang, Seo-Won; Yi, Hahn; Kim, Dae-Won; Kim, Myung-Jin; Byun, Yongik

Description

We determine candidate variable sources in the SuperWASP Data Release 1 (DR1) using multiple clustering methods and identifying variable candidates as outliers from large clusters. We extract 15,788,814 light curves that have more than 15 photometric measurements in the SuperWASP DR1. Variations in the light curves are described in terms of nine variability features that are complementary to each other. We consider three different clustering methods based on Gaussian mixture models, including...[Show more]

dc.contributor.authorShin, Min-Su
dc.contributor.authorChang, Seo-Won
dc.contributor.authorYi, Hahn
dc.contributor.authorKim, Dae-Won
dc.contributor.authorKim, Myung-Jin
dc.contributor.authorByun, Yongik
dc.date.accessioned2019-12-17T01:08:09Z
dc.date.available2019-12-17T01:08:09Z
dc.identifier.issn0004-6256
dc.identifier.urihttp://hdl.handle.net/1885/195629
dc.description.abstractWe determine candidate variable sources in the SuperWASP Data Release 1 (DR1) using multiple clustering methods and identifying variable candidates as outliers from large clusters. We extract 15,788,814 light curves that have more than 15 photometric measurements in the SuperWASP DR1. Variations in the light curves are described in terms of nine variability features that are complementary to each other. We consider three different clustering methods based on Gaussian mixture models, including one that was used in our previous work, assuming that real variable candidates can be found as minor clusters and at a distant from major clusters, which correspond to non-variable objects. The three different methods with a broad level of speed and precision prove that we can select a suitable method for detecting variable light curves, depending on the speed and precision constraints on clustering. We also consider a consensus clustering method that combines clustering results obtained using multiple clustering methods. The consensus clustering method improves the reliability of detecting variable candidates by combining information that is learned from a given data set by multiple methods. As a complete variability analysis of the public SuperWASP light curves, we provide clustering results obtained by using an infinite Gaussian mixture model in the framework of variational Bayesian inference, as well as variability indices of the light curves in an online database to help others exploit the SuperWASP data.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherUniversity of Chicago Press
dc.rights© 2018. The American Astronomical Society
dc.sourceAstronomical Journal
dc.titleDetecting Variability in Massive Astronomical Time-series Data. III. Variable Candidates in the SuperWASP DR1 Found by Multiple Clustering Algorithms and a Consensus Clustering Method
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume156
dc.date.issued2018
local.identifier.absfor020110 - Stellar Astronomy and Planetary Systems
local.identifier.ariespublicationu4485658xPUB2657
local.publisher.urlhttps://aas.org/
local.type.statusPublished Version
local.contributor.affiliationShin, Min-Su, Korea Astronomy and Space Science Institute
local.contributor.affiliationChang, Seo-Won, College of Science, ANU
local.contributor.affiliationYi, Hahn, Asan Medical Center
local.contributor.affiliationKim, Dae-Won, Electronics and Telecommunications Research Institute
local.contributor.affiliationKim, Myung-Jin, Korea Astronomy and Space Science Institute
local.contributor.affiliationByun, Yongik, Yonsei University
local.bibliographicCitation.issue5
local.bibliographicCitation.startpage17
local.bibliographicCitation.lastpage21
local.identifier.doi10.3847/1538-3881/aae263
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.absseo970102 - Expanding Knowledge in the Physical Sciences
dc.date.updated2019-07-28T08:20:18Z
local.identifier.scopusID2-s2.0-85056742844
dcterms.accessRightsOpen Access
dc.provenancehttp://sherpa.ac.uk/romeo/issn/0004-6256/..."author can archive publisher's version/PDF" from SHERPA/RoMEO site (as at 17/12/19).
CollectionsANU Research Publications

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