Benchmark Tests for Markov Chain Monte Carlo Fitting of Exoplanet Eclipse Observations

Ground-based observations of exoplanet eclipses provide important clues to the planets' atmospheric physics, yet systematics in light curve analyses are not fully understood. It is unknown if measurements suggesting near-infrared flux densities brighter than models predict are real, or artifacts of the analysis processes. We created a large suite of model light curves, using both synthetic and real noise, and tested the common process of light curve modeling and parameter optimization with a Markov Chain Monte Carlo (MCMC) algorithm. With synthetic white-noise models, we find that input eclipse signals are generally recovered within 10% accuracy for eclipse depths greater than the noise amplitude, and to smaller depths for higher sampling rates and longer baselines. Red-noise models see greater discrepancies between input and measured eclipse signals, often biased in one direction. Finally, we find that in real data, systematic biases result even with a complex model to account for trends, and significant false eclipse signals may appear in a non-Gaussian distribution. To quantify the bias and validate an eclipse measurement, we compare both the planet-hosting star and several of its neighbors to a separately-chosen control sample of field stars. Re-examining the Rogers et al. (2009) Ks-band measurement of CoRoT-1b finds an eclipse $3190^{+370}_{-440}$ ppm deep centered at $ϕ_{me}$=$0.50418^{+0.00197}_{-0.00203}$. Finally, we provide and recommend the use of selected datasets we generated as a benchmark test for eclipse modeling and analysis routines, and propose criteria to verify eclipse detections.

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