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 Article Swipe
Min‐Su Shin
,
Seo-Won Chang
,
Hahn Yi
,
Dae Won Kim
,
Myeong‐Jin Kim
,
Yong-Ik Byun
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3847/1538-3881/aae263
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3847/1538-3881/aae263
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 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3847/1538-3881/aae263
- https://iopscience.iop.org/article/10.3847/1538-3881/aae263/pdf
- OA Status
- bronze
- Cited By
- 6
- References
- 147
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2895820849
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- OpenAlex ID
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https://openalex.org/W2895820849Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3847/1538-3881/aae263Digital Object Identifier
- Title
-
Detecting Variability in Massive Astronomical Time-series Data. III. Variable Candidates in the SuperWASP DR1 Found by Multiple Clustering Algorithms and a Consensus Clustering MethodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-10-17Full publication date if available
- Authors
-
Min‐Su Shin, Seo-Won Chang, Hahn Yi, Dae Won Kim, Myeong‐Jin Kim, Yong-Ik ByunList of authors in order
- Landing page
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https://doi.org/10.3847/1538-3881/aae263Publisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.3847/1538-3881/aae263/pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://iopscience.iop.org/article/10.3847/1538-3881/aae263/pdfDirect OA link when available
- Concepts
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Cluster analysis, Outlier, Variable (mathematics), Algorithm, Gaussian, Pattern recognition (psychology), Data mining, Mixture model, Correlation clustering, Computer science, Artificial intelligence, Physics, Mathematics, Mathematical analysis, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2023: 2, 2022: 1, 2021: 3Per-year citation counts (last 5 years)
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147Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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