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 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.

Detecting Variability in Massive Astronomical Time-series Data. III. Variable Candidates in the SuperWASP DR1 Found by Multiple Clustering Algorithms and a Consensus Clustering Method

“…σ/μ and σ/ν should be close to each other under the assumption of normal distribution for observed fluxes. However, we find a deviation of σ/ν, which we newly include as a variability index compared to our previous paper (Shin et al 2012), from the distribution of σ/μ.…”

“…Table 1 summarizes the variability indices that are estimated from the flux measurements for given light curves. This combination of indices is similar to that used in our previous papers (Shin et al 2009(Shin et al , 2012; see references therein).σ/μ and σ/ν are the ratio of dispersion over the mean and median, respectively. γ 1 and γ 2 denote the skewness and kurtosis as simple descriptive statistics, respectively.…”

“…We proposed a novel approach that uses clustering methods with density estimation to detect variable candidates in massive astronomical time-series data in our previous papers (Shin et al 2009(Shin et al , 2012. The proposed method can be considered as a complementary method to classical conventional methods.…”

“…Following the methodology used in our two previous papers (Shin et al 2009(Shin et al , 2012, we adopt Gaussian mixture models (Reynolds 2015) to generate clustering structures that become inputs to the ensemble clustering method. Using the same clustering models allows us to apply the ensemble method for the NSVS data included in the previous papers and to try a combined analysis of the NSVS data and the different data examined in this paper together for common objects between the two data sets.…”

Identifying transient and variable sources in radio images

“…One of the most powerful diagnostics is the weighted reduced χ 2 , referred to as η in this paper, of a fit to the light curve by a constant flux density model, which can easily be converted to a probability that the data are drawn from the fitted model (currently used to search for transients at many wavelengths, including X-ray, optical, microwave and radio; e.g. Bannister et al, 2011;Bower et al, 2011;Palanque-Delabrouille et al, 2011;Thyagarajan et al, 2011;Hoffman et al, 2012;Shin et al, 2012;Chen et al, 2013;Croft et al, 2013;Mooley et al, 2013;Williams et al, 2013;Bell et al, 2014;Franzen et al, 2014;Bell et al, 2015). Many surveys utilise a probability threshold to separate the stable sources from the variable sources.…”