### 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 language | English |
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Pages (from-to) | 1897-1910 |

Number of pages | 14 |

Journal | Monthly Notices of the Royal Astronomical Society |

Volume | 400 |

Issue number | 4 |

DOIs | |

Publication status | Published - 2009 Dec 1 |

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### All Science Journal Classification (ASJC) codes

- Astronomy and Astrophysics
- Space and Planetary Science

### Cite this

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*Monthly Notices of the Royal Astronomical Society*, vol. 400, no. 4, pp. 1897-1910. https://doi.org/10.1111/j.1365-2966.2009.15576.x

**Detecting variability in massive astronomical time series data-i. application of an infinite gaussian mixture model.** / Shin, Min Su; Sekora, Michael; Byun, Yong Ik.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Detecting variability in massive astronomical time series data-i. application of an infinite gaussian mixture model

AU - Shin, Min Su

AU - Sekora, Michael

AU - Byun, Yong Ik

PY - 2009/12/1

Y1 - 2009/12/1

N2 - 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.

AB - 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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U2 - 10.1111/j.1365-2966.2009.15576.x

DO - 10.1111/j.1365-2966.2009.15576.x

M3 - Article

AN - SCOPUS:71649097912

VL - 400

SP - 1897

EP - 1910

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 4

ER -