English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present variability analysis of data from the Northern Sky VariabilitySurvey (NSVS). Using the clustering method which defines variable candidates asoutliers from large clusters, we cluster 16,189,040 light curves, having datapoints at more than 15 epochs, as variable and non-variable candidates in 638NSVS fields. Variable candidates are selected depending on how strongly theyare separated from the largest cluster and how rarely they are grouped togetherin eight dimensional space spanned by variability indices. All NSVS lightcurves are also cross-correlated to the Infrared Astronomical Satellite, AKARI,Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GalaxyEvolution Explorer objects as well as known objects in the SIMBAD database. Thevariability analysis and cross-correlation results are provided in a publiconline database which can be used to select interesting objects for furtherinvestigation. Adopting conservative selection criteria for variablecandidates, we find about 1.8 million light curves as possible variablecandidates in the NSVS data, corresponding to about 10% of our entire NSVSsamples. Multi-wavelength colors help us find specific types of variabilityamong the variable candidates. Moreover, we also use morphologicalclassification from other surveys such as SDSS to suppress spurious casescaused by blending objects or extended sources due to the low angularresolution of the NSVS.

Detecting Variability in Massive Astronomical Time-Series Data II: Variable Candidates in the Northern Sky Variability Survey