Asteroseismology of $\delta$ Scuti stars: emulating model grids using a neural network

TL;DR

This paper introduces a neural network emulator combined with Bayesian inference to accurately quantify uncertainties in the stellar parameter estimation of $ ext{delta}$ Scuti stars, addressing challenges in traditional grid-based modeling.

Contribution

It presents a novel method that uses neural network emulation and Bayesian inference to reliably estimate uncertainties in $ ext{delta}$ Scuti star} modeling, including complex posterior distributions.

Findings

Method recovers plausible posterior estimates from simulated stars.

Posterior distributions can be non-Gaussian, multi-modal, and covariant.

Reliable estimation of random uncertainties in $ ext{delta}$ Scuti}$ star modeling.

Abstract

Young $\delta$ Scuti 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 $\delta$ 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, multi-modal, and have strong covariance. We conclude that our method reliably estimates the random uncertainty in the modelling of $\delta$ Sct stars and paves the way for the investigation and quantification of the systematic uncertainty.