News Main branch has been renamed from master to legacy, in preparation of an upcoming major update. The new update will show up soon as main, but legacy will keep staying the default branch until the new version is ready to be distributed.

To update your local clone, run this command on your terminal while in the main directory of PyORBIT:

git branch -m master legacy
git fetch origin
git branch -u origin/legacy legacy
git remote set-head origin -a

All the commands can be executed at once by running the script update_local_clone.sh

News Bayesian evidence estimation can now be performed with:

• dynesty by Josh Speagle dynesty documentation, code repository
• UltraNest by Johannes Buchner UltraNest documentation, code repository
• Several tiny bugfixes here and there

Just substitute "emcee" with "dynesty" or "ultranest" to when running the code. Please check the respective documentation to give proper credit to their work!

• It is now possible to fit planetary transits through batman (Kreidberg 2015) Have a look at Benatti et al. (2019) for more details.

• Examples have been moved to a dedicated repository PyORBIT_example

Warnings

• The output files generated by this version are not retro-compatible (including files generated with version 7.0b), due to some modifications of the internal structure of the code
• Due to the different implementation of inclination and stellar parameters in Transit and RV Dynamical models, at the moment there could be problems when performing transit fit and dynamical modelling at the same time. Additionally, at the moment transit fit does not include Transit Time Variations modelling (i.e., it's not a photodynamical model).
• GetResults needs to be fixed in the part of model extraction

Documentation Some incomplete documentation is available here. For any doubt, feel free to contact me at luca.malavolta_at_inaf.it, 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 models should be used 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).