2022MNRAS.513.2719V

Accurately measuring stellar parameters is a key goal to increase our understanding of the observable Universe. However, current methods are limited by many factors, in particular, the biases and physical assumptions that are the basis for the underlying evolutionary or atmospheric models, those that these methods rely upon. Here, we introduce our code spectrAl eneRgy dIstribution bAyesian moDel averagiNg fittEr (ARIADNE), which tackles this problem by using Bayesian Model Averaging to incorporate the information from all stellar models to arrive at accurate and precise values. This code uses spectral energy distribution fitting methods, combined with precise Gaia distances, to measure the temperature, log g, [Fe/H], A_V, and radius of a star. When compared with interferometrically measured radii ARIADNE produces values in excellent agreement across a wide range of stellar parameters, with a mean fractional difference of only 0.001 ± 0.070. We currently incorporate six different models, and in some cases we find significant offsets between them, reaching differences of up to 550 K and 0.6 R_⊙ in temperature and radius, respectively. For example, such offsets in stellar radius would give rise to a difference in planetary radius of 60 per cent, negating homogeneity when combining results from different models. We also find a trend for stars smaller than 0.4-0.5 R_⊙, which shows more work needs to be done to better model these stars, even though the overall extent is within the uncertainties of the interferometric measurements. We advocate for the use of ARIADNE to provide improved bulk parameters of nearby A to M dwarfs for future studies.