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تسجيل مستخدم جديدDetecting Variability in Massive Astronomical Time-Series Data II: Variable Candidates in the Northern Sky Variability Survey
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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 to the Infrared Astronomical Satellite, AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and Galaxy Evolution Explorer 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.
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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 Bay
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