Probabilistic inference of basic stellar parameters: application to flickering stars

Abstract

The relations between observable stellar parameters are usually assumed to be deterministic. That is, given an infinitely precise measurement of independent variable, `\(x\)', and some model, the value of dependent variable, `\(y\)' can be known exactly. In practise this assumption is rarely valid and intrinsic stochasticity means that two stars with exactly the same `\(x\)', will have slightly different `\(y\)'s. The relation between short-timescale brightness fluctuations (flicker) of stars and both surface gravity and stellar density are two such stochastic relations that have, until now, been treated as deterministic ones. We recalibrate these relations in a probabilistic framework, using Hierarchical Bayesian Modelling (HBM) to constrain the intrinsic scatter in the relations. We find evidence for additional scatter in the relationships, that cannot be accounted for by the observational uncertainties alone. The scatter in surface gravity and stellar density does not depend on flicker, suggesting that using flicker as a proxy for \(\log g\) and \(\rho_\star\) is equally valid for dwarf and giant stars, despite the fact that the observational uncertainties tend to be larger for dwarfs.