Asteroseismology of δ Scuti stars: emulating model grids using a neural network
Young δ Scuti (Sct) stars have proven to be valuable
asteroseismic targets, but obtaining robust uncertainties on
their inferred properties is challenging. We aim to quantify the
random uncertainties in grid-based modelling of
δ Sct stars. We apply Bayesian inference
using nested sampling and a neural network emulator of stellar
models, testing our method on both simulated and real stars.
Based on results from simulated stars, we demonstrate that our
method can recover plausible posterior probability density
estimates while accounting for both the random uncertainty from
the observations and neural network emulation. We find that the
posterior distributions of the fundamental parameters can be
significantly non-Gaussian and multimodal, and have strong
covariance. We conclude that our method reliably estimates the
random uncertainty in the modelling of δ Sct
stars and paves the way for the investigation and quantification
of the systematic uncertainty.