Inferring probabilistic stellar rotation periods using Gaussian processes

@article{Angus2018InferringPS,
  title={Inferring probabilistic stellar rotation periods using Gaussian processes},
  author={Ruth Angus and Timothy D. Morton and Suzanne Aigrain and Daniel Foreman-Mackey and Vinesh M. Rajpaul},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2018},
  volume={474},
  pages={2094-2108}
}
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… Expand
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