TL;DR
This paper introduces a computationally efficient hybrid algorithm for period analysis of multi-band, irregularly sampled light curves, effectively handling sparse data and differing light curve shapes across bands.
Contribution
It presents a novel hybrid method combining two classes of algorithms to improve period detection in multi-band, irregularly sampled astronomical data.
Findings
Effective in identifying periods from sparse, irregular multi-band data
Handles asynchronous measurements across different pass-bands
Demonstrates robustness against sampling beat frequencies
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…
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