Precise radial velocities of giant stars: X. Bayesian stellar parameters and evolutionary stages for 372 giant stars from the Lick planet search* - Stock, Stephan; Reffert, Sabine; Quirrenbach, Andreas (Arxiv, 2018)
The determination of accurate stellar parameters of giant stars is essential for our understanding of such stars in general and as exoplanet host stars in particular. Precise stellar masses are vital for determining the lower mass limit of potential substellar companions with the radial velocity method. Our goal is to determine stellar parameters, including mass, radius, age, surface gravity, effective temperature and luminosity, for the sample of giants observed by the Lick planet search. Furthermore, we want to derive the probability of these stars being on the horizontal branch (HB) or red giant branch (RGB), respectively. We compare spectroscopic, photometric and astrometric observables to grids of stellar evolutionary models using Bayesian inference. We provide tables of stellar parameters, probabilities for the current post-main sequence evolutionary stage, and probability density functions for 372 giants from the Lick planet search. We find that $81%$ of the stars in our sample are more probably on the HB. In particular, this is the case for 15 of the 16 planet host stars in the sample. We tested the reliability of our methodology by comparing our stellar parameters to literature values and find very good agreement. Furthermore, we created a small test sample of 26 giants with available asteroseismic masses and evolutionary stages and compared these to our estimates. The mean difference of the stellar masses for the 24 stars with the same evolutionary stages by both methods is only $\langleΔ M\rangle=0.01±0.20\;\mathrm{M_\odot}$. We do not find any evidence for large systematic differences between our results and estimates of stellar parameters based on other methods. In particular we find no significant systematic offset between stellar masses provided by asteroseismology to our Bayesian estimates based on evolutionary models.
Gesehen am 01.02.2019