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 |
|---|---|
| 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 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Astronomy and Astrophysics
- Space and Planetary Science
Cite this
}
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 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.
UR - http://www.scopus.com/inward/record.url?scp=71649097912&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71649097912&partnerID=8YFLogxK
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 -