  We propose a new information theoretic metric for finding periodicities in
stellar light curves. Light curves are astronomical time series of brightness
over time, and are characterized as being noisy and unevenly sampled. The
proposed metric combines correntropy (generalized correlation) with a periodic
kernel to measure similarity among samples separated by a given period. The new
metric provides a periodogram, called Correntropy Kernelized Periodogram (CKP),
whose peaks are associated with the fundamental frequencies present in the
data. The CKP does not require any resampling, slotting or folding scheme as it
is computed directly from the available samples. CKP is the main part of a
fully-automated pipeline for periodic light curve discrimination to be used in
astronomical survey databases. We show that the CKP method outperformed the
slotted correntropy, and conventional methods used in astronomy for periodicity
discrimination and period estimation tasks, using a set of light curves drawn
from the MACHO survey. The proposed metric achieved 97.2% of true positives
with 0% of false positives at the confidence level of 99% for the periodicity
discrimination task; and 88% of hits with 11.6% of multiples and 0.4% of misses
in the period estimation task.
