  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 $\phi_{me}$=$0.50418^{+0.00197}_{-0.00203}$. 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.

Earth and Planetary Astrophysics