Min-Su Shin, Hahn Yi, Dae-Won Kim, Seo-Won Chang, Yong-Ik Byun
We present variability analysis of data from the Northern Sky Variability
Survey. 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 to the IRAS, 2MASS, 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 samples. 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.
View original:
http://arxiv.org/abs/1111.1804
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