| dc.description.abstract | 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],
AV, 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%, 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. | |
