A Bayesian Surrogate Model for Rapid Time Series Analysis and Application to Exoplanet Observations

2011/07/20 by Eric B. Ford, Ford, Eric B., Althea V. Moorhead +3
Computer Science · #Applications (stat.AP) #Blind Source Separation Techniques #Computation (stat.CO) #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1107.4047

openalex publication_date 2011/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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