pomme

Welcome to the pomme documentation!

Contents:

• Background
• Examples
• Python API

About

pomme is a python package that allows you to create probabilistic 3D reconstructions of astronomical spectral line observations.

Observations of spectral lines are indespensible in astronomy, since they encode a wealth of information about the physical and chemical conditions of the medium from which they originate. For instance, their narrow extent in frequency space make them very sensitive to Doppler shifts, such that their shape encodes the motion of the medium along the line of sight. As a result, given a good model for the line formation process and an inversion method, these physical and chemical properties can be retrieved from observations. Currently, we mainly focus on retrieving the distributions of the abundance of the chemical species producing the line, the velocity field, and its kinetic temperature. However, also other parameters can be retrieved.

More information about the model for spectral line formation and the probabilistic reconstruction methods can be found in the background pages.

pomme is built on top of PyTorch and benefits a lot from functionality provided by Astropy. It is currently developed and maintained by Frederik De Ceuster at KU Leuven.

Installation

Get the latest release (version 0.0.15) either from PyPI, using pip, with:

pip install pomme

or from Anaconda.org, using conda, with:

conda install -c freddeceuster pomme

or download the source code, unzip, and install with pip by executing:

pip install .

in the root directory of the code.

Issues

Please report any issues with this software or its documentation here.

Contributing

We are open to contributions to pomme. More information can be found here.

Collaborating

We are always interested in collaborating! If you like our work but it needs some tailoring for your specific use case feel free to contact me.

Acknowledgements

Frederik De Ceuster is a Postdoctoral Research Fellow of the Research Foundation - Flanders (FWO), grant number 1253223N, and was previously supported for this research by a Postdoctoral Mandate (PDM) from KU Leuven, grant number PDMT2/21/066.