A novel tool for the spectroscopic inference of fundamental stellar parameters

We present a novel approach for making accurate and unbiased inferences of fundamental stellar parameters (e.g., effective temperature, surface gravity, metallicity) from spectroscopic observations, with reference to a library of synthetic spectra. The forward-modeling formalism we have developed is generic (easily adaptable to data from any instrument or covering any wavelength range) and modular, in that it can incorporate external prior knowledge or additional data (e.g., broadband photometry) and account for instrumental and non-stellar effects on the spectrum (e.g., parametric treatments of extinction, spots, etc.). We use covariance kernels to account for systematic discrepancies between the observations and the synthetic spectral library, ensuring that issues like uncertainties in atomic or molecular constants do not strongly bias the parameter inferences. In addition to extracting a set of unbiased inferences of the (posterior) probability distributions for basic stellar parameters, our modeling approach also "maps" out problematic spectral regions in the synthetic libraries that could be used as a basis for improving the models. As a demonstration, we present some preliminary results from modeling optical spectra of well-characterized exoplanet host stars and nearby pre-main sequence stars. A basic set of adaptable software that performs this modeling approach will be released publicly.

Presented at the 224th AAS meeting in Boston, June 4th, 2014.