# Inferring probabilistic stellar rotation periods using Gaussian processes

## TL;DR

This paper introduces a Gaussian Process-based method to accurately infer stellar rotation periods from light curves, outperforming traditional techniques and providing valuable posterior distributions for astrophysical studies.

## Contribution

The paper presents a novel Gaussian Process approach with a quasi-periodic kernel for inferring stellar rotation periods, enabling more accurate and probabilistic analysis compared to existing methods.

## Key findings

- Gaussian Process method outperforms sine-fitting and autocorrelation methods
- Successfully inferred rotation periods for simulated and real Kepler data
- Provides posterior distributions for rotation periods facilitating hierarchical studies

## Abstract

Variability in the light curves of spotted, rotating stars is often non-sinusoidal and quasi-periodic --- spots move on the stellar surface and have finite lifetimes, causing stellar flux variations to slowly shift in phase. A strictly periodic sinusoid therefore cannot accurately model a rotationally modulated stellar light curve. Physical models of stellar surfaces have many drawbacks preventing effective inference, such as highly degenerate or high-dimensional parameter spaces. In this work, we test an appropriate effective model: a Gaussian Process with a quasi-periodic covariance kernel function. This highly flexible model allows sampling of the posterior probability density function of the periodic parameter, marginalising over the other kernel hyperparameters using a Markov Chain Monte Carlo approach. To test the effectiveness of this method, we infer rotation periods from 333 simulated stellar light curves, demonstrating that the Gaussian process method produces periods that are more accurate than both a sine-fitting periodogram and an autocorrelation function method. We also demonstrate that it works well on real data, by inferring rotation periods for 275 Kepler stars with previously measured periods. We provide a table of rotation periods for these 1132 Kepler objects of interest and their posterior probability density function samples. Because this method delivers posterior probability density functions, it will enable hierarchical studies involving stellar rotation, particularly those involving population modelling, such as inferring stellar ages, obliquities in exoplanet systems, or characterising star-planet interactions. The code used to implement this method is available online.