  By detecting light from extrasolar planets,we can measure their compositions
and bulk physical properties. The technologies used to make these measurements
are still in their infancy, and a lack of self-consistency suggests that
previous observations have underestimated their systemic errors.We demonstrate
a statistical method, newly applied to exoplanet characterization, which uses a
Bayesian formalism to account for underestimated errorbars. We use this method
to compare photometry of a substellar companion, GJ 758b, with custom
atmospheric models. Our method produces a probability distribution of
atmospheric model parameters including temperature, gravity, cloud model
(fsed), and chemical abundance for GJ 758b. This distribution is less sensitive
to highly variant data, and appropriately reflects a greater uncertainty on
parameter fits.
