Welcome to the pyhonradex documentation!

pythonradex solves the radiative transfer for a uniform medium in non-LTE with an escape probability formalism. The code can be used to quickly estimate the emission from an astrophysical gas given input parameters such as the kinetic temperature, the column density and the density of the collision partners.

pythonradex is a python re-implementation of the RADEX code [vanderTak07]. Depending on the setup, it can be faster than RADEX when calculating a grid of models. It also provides additional functionality that is not included in RADEX (treatment of overlapping lines, treatment of internal dust continuum, output of spectra,…).

pyhonradex also provides a convenient method to read files in the LAMDA format (e.g. from the EMAA or LAMDA databases).

Please see the example notebooks for an overview of the functionalities offered by pythonradex.

Contents:

• Installation
• Examples
  • Getting started with pythonradex
  • A simple example
  • Grid search
  • Line core saturation by high optical depth
  • Overlapping lines
  • LVG geometry
  • Continuum effects
  • Reading LAMDA-formatted files
• Benchmark vs RADEX
  • Comparing the outputs of pythonradex and RADEX
  • Performance comparison between RADEX and pythonradex
• Theory
  • Basic radiative transfer
  • Accelerated Lambda Iteration (ALI)
  • The ALI method by Rybicki & Hummer (1992)
  • Escape probability
  • Ng-acceleration
• Source geometries
  • Static sphere
  • Static slab
  • LVG sphere
  • LVG slab
  • Geometries emulating RADEX
  • The line width parameter width_v
• Differences between pythonradex and RADEX
  • Overlapping lines and internal continuum
  • Programming language and performance
  • Different flux output
  • Different specific intensity and flux for spherical geometry
  • Flux calculation
  • Different escape probability for LVG sphere
  • Different behaviour for H₂ collider densities
• FAQ
  • How should negative optical depth be interpreted?
  • I get an “Underlying object has vanished” error
• Contact
• API
  • The Source class
  • Reading LAMDA files
  • Representation of molecular levels and transitions
  • Representation of line profiles
  • Convenience functions
• Citation

Acknowledgement

I would like to thank Simon Bruderer for his helpful clarifications about the ALI method. I also thank Andrés Asensio Ramos for helpful discussions about the LVG geometry and help with the MOLPOP-CEP code that was used to benchmark pythonradex.

Bibliography

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de Jong, T., Boland, W., & Dalgarno, A. 1980, Astronomy and Astrophysics, 91, 68

[deJong75]

de Jong, T., Chu, S., & Dalgarno, A. 1975, Astrophysical Journal, 199, 69

[Elitzur92]

Elitzur, M. 1992, Astronomical masers, ISBN 978-0-7923-1217-8, Springer Dordrecht

[Goldreich74]

Goldreich, P., & Kwan, J. 1974, Astrophysical Journal, 189, 441

[Hubeny03]

Hubeny, I. 2003, Accelerated Lambda Iteration: An Overview. Stellar Atmosphere Modeling 288, 17

[Ng74]

Ng, K.-C. 1974, Journal of Chemical Physics, 61, 2680

[Osterbrock74]

Osterbrock, D. E. 1974, Astrophysics of Gaseous Nebulae, ISBN 0-716-70348-3, W. H. Freeman

[Rybicki91]

Rybicki, G. B., & Hummer, D. G. 1991, Astronomy and Astrophysics, 245, 171

[Rybicki92]

Rybicki, G. B., & Hummer, D. G. 1992, Astronomy and Astrophysics, 262, 209

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Rybicki, G. B., & Lightman, A.P. 2004, Radiative Processes in Astrophysics, ISBN 0-471-82759-2, Wiley-VCH

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Scoville, N. Z., & Solomon, P. M. 1974, Astrophysical Journal, 187, L67

[vanderTak07]

van der Tak, F. F. S., Black, J. H., Schöier, F. L., Jansen, D. J., & van Dishoeck, E. F. 2007, Astronomy and Astrophysics, 468, 627