Towards a Spectral Technique for Determining Material Geometry Around Evolved Stars: Application to HD 179821

TL;DR

This paper develops a spectral technique to differentiate between dust shell and disc geometries around evolved stars by modeling their spectral energy distributions, applied specifically to HD 179821.

Contribution

It introduces a method combining radiative transfer models to distinguish circumstellar geometries based on spectral data, applied to a case study of HD 179821.

Findings

Both models fit short-wavelength data equally well.

Long-wavelength data favors a spherical dust shell over a disc.

HD 179821 likely has a hotter central star than previously estimated.

Abstract

HD 179821 is an evolved star of unknown progenitor mass range (either post-Asymptotic Giant Branch or post-Red Supergiant) exhibiting a double peaked spectral energy distribution (SED) with a sharp rise from $\sim8-20$ $\mu$m. Such features have been associated with ejected dust shells or inwardly truncated circumstellar discs. In order to compare SEDs from both systems, we employ a spherically symmetric radiative transfer code and compare it to a radiative, inwardly truncated disc code. As a case study, we model the broad-band SED of HD 179821 using both codes. Shortward of 40 $\mu$m, we find that both models produce equivalent fits to the data. However, longward of 40 $\mu$m, the radial density distribution and corresponding broad range of disc temperatures produce excess emission above our spherically symmetric solutions and the observations. For HD 179821, our best fit consists of a $T_{eff}=7000$ K central source characterized by $\tau_V\sim1.95$ and surrounded by a radiatively driven, spherically symmetric dust shell. The extinction of the central source reddens the broad-band colours so that they resemble a $T_{eff}=5750$ K photosphere. We believe that HD 179821 contains a hotter central star than previously thought. Our results provide an initial step towards a technique to distinguish geometric differences from spectral modeling.