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Ongoing and future surveys with repeat imaging in multiple bands areproducing (or will produce) time-spaced measurements of brightness, resultingin the identification of large numbers of variable sources in the sky. A largefraction of these are periodic variables: compilations of these are ofscientific interest for a variety of purposes. Unavoidably, the data-sets frommany such surveys not only have sparse sampling, but also have embeddedfrequencies in the observing cadence that beat against the naturalperiodicities of any object under investigation. Such limitations can makeperiod determination ambiguous and uncertain. For multi-band data sets withasynchronous measurements in multiple pass-bands, we want to maximally utilizethe information on periodicity in a manner that is agnostic of differences inthe light curve shapes across the different channels. Given large volumes ofdata, computational efficiency is also at a premium. This paper develops andpresents a computationally economic method for determining periodicity whichcombines the results from two different classes of period determinationalgorithms. The underlying principles are illustrated through examples. Theeffectiveness of this approach for combining asynchronously sampledmeasurements in multiple observables that share an underlying fundamentalfrequency is also demonstrated.

A Hybrid Algorithm for Period Analysis from Multi-band Data with Sparse and Irregular Sampling for Arbitrary Light Curve Shapes