Bayesian Model Reconstruction Based on Spectral Line Observations

Abstract

Spectral line observations encode a wealth of information. A key challenge, therefore, lies in the interpretation of these observations in terms of models to derive the physical and chemical properties of the astronomical environments from which they arise. In this paper, we present pomme , an open-source Python package that allows users to retrieve 1D or 3D models of physical properties, such as chemical abundance, velocity, and temperature distributions of (optically thin) astrophysical media, based on spectral line observations. We discuss how prior knowledge, for instance, in the form of a steady-state hydrodynamics model, can be used to guide the retrieval process, and we demonstrate our methods on both synthetic and real observations of cool stellar winds.

Keywords

• Astronomy software
• Radiative transfer simulations
• Computational methods
• Rotational spectroscopy