Detecting variability in massive astronomical time-series data. II. Variable candidates in the Northern Sky Variability Survey

Min Su Shin, Hahn Yi, Dae Won Kim, Seo Won Chang, Yong Ik Byun

Research output: Contribution to journalReview article

10 Citations (Scopus)

Abstract

We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method, which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves having data points at more than 15 epochs as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight-dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated with IRAS, AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GALEX objects, as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database, which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10% of our entire NSVS sample. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSVS.

Original languageEnglish
Article number65
JournalAstronomical Journal
Volume143
Issue number3
DOIs
Publication statusPublished - 2012 Mar 1

Fingerprint

northern sky
time series
light curve
outlier
Infrared Astronomy Satellite
field survey
angular resolution
cross correlation
wavelength
time measurement

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

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title = "Detecting variability in massive astronomical time-series data. II. Variable candidates in the Northern Sky Variability Survey",
abstract = "We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method, which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves having data points at more than 15 epochs as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight-dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated with IRAS, AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GALEX objects, as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database, which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10{\%} of our entire NSVS sample. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSVS.",
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Detecting variability in massive astronomical time-series data. II. Variable candidates in the Northern Sky Variability Survey. / Shin, Min Su; Yi, Hahn; Kim, Dae Won; Chang, Seo Won; Byun, Yong Ik.

In: Astronomical Journal, Vol. 143, No. 3, 65, 01.03.2012.

Research output: Contribution to journalReview article

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