When good fits go wrong: determining realistic best fits and uncertainties on L dwarf physical parameters

Synthetic spectra can be found that reproduce observed L dwarf data reasonably well. However, the parameters implied by these "best-fit" spectra are often unphysical.

We find that some regions of low-resolution data yield results consistent with empirical measurements of effective temperature.

We find that models also yield reliable temperatures for certain spectral sub-types.

We employ Markov-chain Monte Carlo (MCMC) to robustly characterize degeracies between model parameters and uncertainties on best-fit values.

Our test sample consists of 10 normal L dwarfs with low-resolution SpeX Prism data; we also have moderate and high-resolution data for these L dwarfs.

In future, we will fit models to a larger sample including many more low- and moderate-resolution spectra.