  The ability to automatically and robustly self-verify periodicity present in
time-series astronomical data is becoming more important as data sets rapidly
increase in size. The age of large astronomical surveys has rendered manual
inspection of time-series data less practical. Previous efforts in generating a
false alarm probability to verify the periodicity of stars have been aimed
towards the analysis of a constructed periodogram. However, these methods
feature correlations with features that do not pertain to periodicity, such as
light curve shape, slow trends and stochastic variability. The common
assumption that photometric errors are Gaussian and well determined is also a
limitation of analytic methods. We present a novel machine learning based
technique which directly analyses the phase folded light curve for its false
alarm probability. We show that the results of this method are largely
insensitive to the shape of the light curve, and we establish minimum values
for the number of data points and the amplitude to noise ratio.
