Estimating a Common Period for a Set of Irregularly Sampled Functions with Applications to Periodic Variable Star Data

We consider the estimation of a common period for a set of functions sampled at irregular intervals. The problem arises in astronomy, where the functions represent a star's brightness observed over time through different photometric filters. While current methods can estimate periods accurately provided that the brightness is well--sampled in at least one filter, there are no existing methods that can provide accurate estimates when no brightness function is well--sampled. In this paper we introduce two new methods for period estimation when brightnesses are poorly--sampled in all filters. The first, multiband generalized Lomb-Scargle (MGLS), extends the frequently used Lomb-Scargle method in a way that naïvely combines information across filters. The second, penalized generalized Lomb-Scargle (PGLS), builds on the first by more intelligently borrowing strength across filters. Specifically, we incorporate constraints on the phases and amplitudes across the different functions using a non--convex penalized likelihood function. We develop a fast algorithm to optimize the penalized likelihood by combining block coordinate descent with the majorization-minimization (MM) principle. We illustrate our methods on synthetic and real astronomy data. Both advance the state-of-the-art in period estimation; however, PGLS significantly outperforms MGLS when all functions are extremely poorly--sampled.

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