PyORBIT: a code for exoplanet orbital parameters and stellar activity.
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PyORBIT version 6.0 by Luca Malavolta - September 2018

New Version 6 correctly implements model selection with nested sampling performed by MultiNest/PolyChord. Because of the way the software has been structured, each model must be contained in a different yaml file, i.e. the number of planets is not an hyperparameter but it must be fixed a priori. However, the same yaml file can be used to run emcee, MultiNest or PolyChord independently.

Documentation Some incomplete documentation is available here. For any doubt, feel free to contact me at, I'll be happy to work out together any problem that may arise during installation or usage of this software.

PyORBIT handles several kinds of datasets, such as radial velocity (RV), activity indexes, and photometry, to simultaneously characterize the orbital parameters of exoplanets and the noise induced by the activity of the host star. RV computation is performed using either non-interacting Kepler orbits or n-body integration. Stellar activity can be modeled either with sinusoids at the rotational period and its harmonics or gaussian process. Offsets and systematics in measurements from several instruments can be modeled as well. Thanks to the modular approach, new methods for stellar activity modeling or parameter estimation can be easily incorporated into the code.

Models Any of these models can be applied to a dataset. The user can choose which model should be use for each dataset.

  • Gaussian Processes for RV or photometry (shared or independent hyperparameter)
  • Polynomial trends with user-defined order
  • Correlation with activity indexes (or any other dataset)
  • Sinusoids (independent or shared amplitudes and periods)
  • Celerite support available (untested)

Priors These priors can be applied to any of the parameters (it's up to the user to choice the appropriate ones):

  • Uniform default prior for all the parameters
  • Gaussian
  • Jeffreys
  • Modified Jeffreys
  • Truncated Rayleigh
  • WhiteNoisePrior
  • BetaDistribution

Jeffreys and Modified Jeffreys priors are actually Truncated Jeffreys and Truncated Modified Jeffreys, with truncation defined by the boundaries of the parameter space.

Parameter exploration The user can choice between Linear and Logarithmic. Note that in the second case the parameter space is transformed into base-2 logarithm.

Most of the information can be found in Malavolta et al. (2016) and Malavolta et al. (2018).