Title: A Hybrid Algorithm for Period Analysis from Multi-band Data with Sparse and Irregular Sampling for Arbitrary Light Curve Shapes,
Abstract: Ongoing and future surveys with repeat imaging in multiple bands are
producing (or will produce) time-spaced measurements of brightness, resulting
in the identification of large numbers of variable sources in the sky. A large
fraction of these are periodic variables: compilations of these are of
scientific interest for a variety of purposes. Unavoidably, the data-sets from
many such surveys not only have sparse sampling, but also have embedded
frequencies in the observing cadence that beat against the natural
periodicities of any object under investigation. Such limitations can make
period determination ambiguous and uncertain. For multi-band data sets with
asynchronous measurements in multiple pass-bands, we want to maximally utilize
the information on periodicity in a manner that is agnostic of differences in
the light curve shapes across the different channels. Given large volumes of
data, computational efficiency is also at a premium. This paper develops and
presents a computationally economic method for determining periodicity which
combines the results from two different classes of period determination
algorithms. The underlying principles are illustrated through examples. The
effectiveness of this approach for combining asynchronously sampled
measurements in multiple observables that share an underlying fundamental
frequency is also demonstrated.