A new statistical method for characterizing the atmospheres of extrasolar planets
  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.
