Bayesian model reconstruction based on spectral line observations
arxiv(2024)
摘要
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
demonstrate our methods both on synthetic and real observations of cool stellar
winds.
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