Period estimation for sparsely-sampled quasi-periodic light curves applied to Miras

TL;DR

This paper introduces a semi-parametric Gaussian process model for estimating periods of sparsely-sampled Mira variable stars, improving accuracy over existing methods by effectively handling stochastic variations and multimodal likelihoods.

Contribution

The paper presents a novel non-linear semi-parametric Gaussian process approach that enhances period estimation for sparsely-sampled light curves, with a robust hybrid optimization method.

Findings

Outperforms existing algorithms in period recovery rate

Produces higher quality Period-Luminosity relations

Validated on a large simulated dataset

Abstract

We develop a non-linear semi-parametric Gaussian process model to estimate periods of Miras with sparsely-sampled light curves. The model uses a sinusoidal basis for the periodic variation and a Gaussian process for the stochastic changes. We use maximum likelihood to estimate the period and the parameters of the Gaussian process, while integrating out the effects of other nuisance parameters in the model with respect to a suitable prior distribution obtained from earlier studies. Since the likelihood is highly multimodal for period, we implement a hybrid method that applies the quasi-Newton algorithm for Gaussian process parameters and search the period/frequency parameter over a dense grid. A large-scale, high-fidelity simulation is conducted to mimic the sampling quality of Mira light curves obtained by the M33 Synoptic Stellar Survey. The simulated data set is publicly available and can serve as a testbed for future evaluation of different period estimation methods. The semi-parametric model outperforms an existing algorithm on this simulated test data set as measured by period recovery rate and quality of the resulting Period-Luminosity relations.