Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 474, Issue 2, Pages 2094-2108Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stx2109
Keywords
methods: data analysis; methods: statistical; techniques: photometric; stars: rotation; stars: solar-type; starspots
Categories
Funding
- Simons Foundation [429961]
- NASA [NNX14AE11G, NAS5-26555]
- Merton College
- National Research Foundation of South Africa
- NASA Office of Space Science [NNX09AF08G]
- NASA Science Mission directorate
- STFC [ST/N000919/1] Funding Source: UKRI
- NASA [685437, NNX14AE11G] Funding Source: Federal RePORTER
Ask authors/readers for more resources
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, marginalizing 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 and many more, altogether 1102 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 characterizing star-planet interactions. The code used to implement this method is available online.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available