Work

Bayesian model reconstruction based on spectral line observations with pomme

Year: 2024
Type: article
Abstract: A typical problem in astronomy is that, for most of our observations, we are restricted to the plane of the sky.As a result, these observations are always mere projections containing only partial information about the observed object.Luckily, some frequency bands of the electromagnetic spectrum are optically thin, such that we receive radiation from the entire medium along the line of sight.This means that, at least in principle, from the observed radiation, we can extract information about the physical and chemical conditions along the entire line of sight.This is especially the case for spectral line radiation caused by transitions between the quantized energy levels of atoms and molecules.Rotational transition lines are particularly interesting, since they are excited in many astrophysical environments and can easily be resolved individually.Using spectral line observations, we can infer information about physical and chemical parameters, such as abundance of certain molecules, velocity, and temperature distributions.To facilitate this, we built a Python package, called pomme, that helps users reconstruct 1D spherically symmetric or generic 3D models, based on astronomical spectral line observations.A detailed description and validation of the methods can be found in De Ceuster et al. (2024).
Language: English
Open Access status: diamond
OpenAlex ID: W4405209956
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Cites: 7
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Related to: 10