Periodograms for Multiband Astronomical Time Series

TL;DR

This paper presents the multiband periodogram, an extension of Lomb-Scargle for better period detection in multiband astronomical time series, demonstrating improved accuracy with simulated and real data, especially for LSST observations.

Contribution

The paper introduces the multiband periodogram using Tikhonov regularization, enhancing period detection in multiband light curves by reducing model complexity and improving performance over existing methods.

Findings

Outperforms existing methods in period detection accuracy.

Effective with as little as six months of LSST data.

Demonstrated on simulated and SDSS Stripe 82 data.

Abstract

This paper introduces the multiband periodogram, a general extension of the well-known Lomb-Scargle approach for detecting periodic signals in time-domain data. In addition to advantages of the Lomb-Scargle method such as treatment of non-uniform sampling and heteroscedastic errors, the multiband periodogram significantly improves period finding for randomly sampled multiband light curves (e.g., Pan-STARRS, DES and LSST). The light curves in each band are modeled as arbitrary truncated Fourier series, with the period and phase shared across all bands. The key aspect is the use of Tikhonov regularization which drives most of the variability into the so-called base model common to all bands, while fits for individual bands describe residuals relative to the base model and typically require lower-order Fourier series. This decrease in the effective model complexity is the main reason for improved performance. We use simulated light curves and randomly subsampled SDSS Stripe 82 data to demonstrate the superiority of this method compared to other methods from the literature, and find that this method will be able to efficiently determine the correct period in the majority of LSST's bright RR Lyrae stars with as little as six months of LSST data. A Python implementation of this method, along with code to fully reproduce the results reported here, is available on GitHub.