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Welcome

kima is the name of a really good drink produced in Azores 🍹
And now, it's also a package for the analysis of radial velocity (RV) data.

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In brief, kima fits Keplerian curves to a set of RV measurements, using the Diffusive Nested Sampling algorithm1 to sample the posterior distribution for the model parameters. Additionally, the code can calculate the fully marginalized likelihood (or evidence, \(Z\)) of a model with a given number of Keplerians and also infer the number (\(N_p\)) of Keplerian signals detected in a given dataset. In this case, \(N_p\) is a free parameter and its posterior distribution is estimated2, 3.

The code is under active development at this GitHub repository.
This page hosts the documentation, examples, and detailed APIs.

If you use kima in your work or research, we kindly ask that you include the following citation4

Faria et al., (2018). kima: Exoplanet detection in radial velocities.
Journal of Open Source Software, 3(26), 487

Feel free to use the following BibTeX entry, or see the ADS entry 2018JOSS....3..487F.

@article{kima,
      title = {kima: Exoplanet detection in radial velocities},
      author = {Faria, J. P. and Santos, N. C. and Figueira, P. and Brewer, B. J.},
      journal = {Journal of Open Source Software},
      issn = {2475-9066},
      number = {26},
      volume = {3},
      pages = {487},
      date = {2018-06-19},
      year = {2018},
      month = {6},
      day = {19},
      publisher = {The Open Journal},
      doi = {10.21105/joss.00487},
      url = {http://dx.doi.org/10.21105/joss.00487},
}

References

  1. Brewer, B.J., Pártay, L.B. & Csányi, G. Diffusive Nested Sampling. Stat Comput 21, 649–656 (2011)
    arXiv, DOI ↩

  2. Brewer, B.J., Inference for Trans-dimensional Bayesian Models with Diffusive Nested Sampling (2015)
    arXiv ↩

  3. Brewer, B.J. and Donovan, C.P., Fast Bayesian inference for exoplanet discovery in radial velocity data, MNRAS 448, 4, 3206–3214 (2015)
    arXiv, DOI ↩

  4. Faria et al., kima: Exoplanet detection in radial velocities. Journal of Open Source Software, 3(26), 487 (2018)
    arXiv, DOI ↩