Detecting variability in massive astronomical time series data-i. application of an infinite gaussian mixture model

Min Su Shin, Michael Sekora, Yong-Ik Byun

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

We present a new framework to detect various types of variable objects within massive astronomical time series data. Assuming that the dominant population of objects is non-variable, we find outliers from this population by using a non-parametric Bayesian clustering algorithm based on an infinite Gaussian mixture model (GMM) and the Dirichlet process. The algorithm extracts information from a given data set, which is described by six variability indices. The GMM uses those variability indices to recover clusters that are described by six-dimensional multivariate Gaussian distributions, allowing our approach to consider the sampling pattern of time series data, systematic biases, the number of data points for each light curve and photometric quality. Using the Northern Sky Variability Survey data, we test our approach and prove that the infinite GMM is useful at detecting variable objects, while providing statistical inference estimation that suppresses false detection. The proposed approach will be effective in the exploration of future surveys such as Gaia, Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and Large Synoptic Survey Telescope (LSST), which will produce massive time series data.

Original languageEnglish
Pages (from-to)1897-1910
Number of pages14
JournalMonthly Notices of the Royal Astronomical Society
Volume400
Issue number4
DOIs
Publication statusPublished - 2009 Dec 1

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time series
northern sky
telescopes
inference
normal density functions
light curve
outlier
sampling
index

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

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abstract = "We present a new framework to detect various types of variable objects within massive astronomical time series data. Assuming that the dominant population of objects is non-variable, we find outliers from this population by using a non-parametric Bayesian clustering algorithm based on an infinite Gaussian mixture model (GMM) and the Dirichlet process. The algorithm extracts information from a given data set, which is described by six variability indices. The GMM uses those variability indices to recover clusters that are described by six-dimensional multivariate Gaussian distributions, allowing our approach to consider the sampling pattern of time series data, systematic biases, the number of data points for each light curve and photometric quality. Using the Northern Sky Variability Survey data, we test our approach and prove that the infinite GMM is useful at detecting variable objects, while providing statistical inference estimation that suppresses false detection. The proposed approach will be effective in the exploration of future surveys such as Gaia, Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and Large Synoptic Survey Telescope (LSST), which will produce massive time series data.",
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Detecting variability in massive astronomical time series data-i. application of an infinite gaussian mixture model. / Shin, Min Su; Sekora, Michael; Byun, Yong-Ik.

In: Monthly Notices of the Royal Astronomical Society, Vol. 400, No. 4, 01.12.2009, p. 1897-1910.

Research output: Contribution to journalArticle

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