A Bayesian Surrogate Model for Rapid Time Series Analysis and Application to Exoplanet Observations
Methodology
2011-07-21 v1 Earth and Planetary Astrophysics
Instrumentation and Methods for Astrophysics
Applications
Computation
Abstract
We present a Bayesian surrogate model for the analysis of periodic or quasi-periodic time series data. We describe a computationally efficient implementation that enables Bayesian model comparison. We apply this model to simulated and real exoplanet observations. We discuss the results and demonstrate some of the challenges for applying our surrogate model to realistic exoplanet data sets. In particular, we find that analyses of real world data should pay careful attention to the effects of uneven spacing of observations and the choice of prior for the "jitter" parameter.
Cite
@article{arxiv.1107.4047,
title = {A Bayesian Surrogate Model for Rapid Time Series Analysis and Application to Exoplanet Observations},
author = {Eric B. Ford and Althea V. Moorhead and Dimitri Veras},
journal= {arXiv preprint arXiv:1107.4047},
year = {2011}
}
Comments
25 pages, 4 figures, accepted to Bayesian Analysis <http://ba.stat.cmu.edu>, special issue for Ninth Valencia International Conference on Bayesian Statistics