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An efficient Python implementation for Bayesian inference in binary stars based on Stan.

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BinaryStars

License: New BSD

An efficient Python implementation for Bayesian inference in binary stars based on the probabilistic programming language Stan.

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Contents

This repository includes the implementation of the statistical models for different binary stellar systems configurations:

  • Visual Binary model (visual.stan).
  • SB2 model (sb2.stan).
  • Visual-SB2 model (visual_sb2.stan).
  • Visual-SB1 model (visual_sb1.stan).

Requirements

Usage

See the examples/Visual_SB2.ipynb notebook for the instructions usage. To incorporate prior distributions see the examples/Visual_SB1_priors.ipynb notebook example.

References

[1] Videla, M., Mendez, R. A., Claveria, R. M., Silva, J. F., & Orchard, M. E. (2022). Bayesian inference in single-line spectroscopic binaries with a visual orbit. The Astronomical Journal, 163(5).

[2] Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1).