Octofitter

Octofitter is a Julia package for performing Bayesian inference against a wide variety of exoplanet and binary star data.

Supported data: Fit exoplanet orbits to:

• relative astrometry
• absolute astrometry (Gaia or Hipparcos)
• absolute radial velocity data (of a star)
• relative radial velocity data (of a planet relative to a star, or planet relative to another planet)
• sample directly from images (also known as "deorbiting")
• sample directly from interferometric visibilities (NIRISS-AMI or GRAVITY)

You can freely combine any of the above data types. Any and all combinations work together.

Modelling features:

• multiple planets (zero or more); marginalize over the number of planets (transdimensional models)
• optional drop-in N-body integration
• arbitrary hierarchies: moons, circumbinary planets, 2+2 quadruples
• pulsar-timing grade kinematics (perspective acceleration, viewing angle changes, Rømer delay, differential light travel time between planets) opted in automatically
• gaussian processes (AbstractGPs or Celerite)
• hyperbolic orbits
• arbitrary priors and parameterizations
• optional O'Neil "observable based priors"
• link mass to photometry and images via atmosphere models
• hierarchical models - e.g. link planets' orbital planes through a system-level hyperprior

Speed:

Fit astrometry on your laptop in seconds!

• Highly optimized code and derivatives are generated from your model
• Higher order sampler (No U-Turn sampler) which explores the parameter space very efficiently
• Samplers warmed up automatically using a variational approximation from the Pathfinder algorithm (Pathfinder.jl)

See also: the python libraries Orbitize!, orvara, and exoplanet.

Read the paper

In addition to these documentation and tutorial pages, you can read the paper published in the Astronomical Journal (open-access).

Attribution

• If you use Octofitter in your work, please cite Thompson et al:

@article{Thompson_2023,
doi = {10.3847/1538-3881/acf5cc},
url = {https://dx.doi.org/10.3847/1538-3881/acf5cc},
year = {2023},
month = {sep},
publisher = {The American Astronomical Society},
volume = {166},
number = {4},
pages = {164},
author = {William Thompson and Jensen Lawrence and Dori Blakely and Christian Marois and Jason Wang and Mosé Giordano and Timothy Brandt and Doug Johnstone and Jean-Baptiste Ruffio and S. Mark Ammons and Katie A. Crotts and Clarissa R. Do Ó and Eileen C. Gonzales and Malena Rice},
title = {Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets},
journal = {The Astronomical Journal},
}

• If you use Gaia parallaxes in your work, please cite Gaia DR3 Gaia Collaboration et al. 2023
• Please cite the HMC sampler backend if you use octofit: Xu et al 2020
• Please cite the Pigeons paper if you use octofit_pigeons.
• If you use Hipparcos-GAIA proper motion anomaly, please cite Brandt 2021
• If you use example data in one of the tutorials, please cite the sources listed
• If you use one of the included functions for automatically retreiving data from a public dataset, eg HARPS RVBank, please cite the source as appropriate (it will be displayed in the terminal)
• If you adopt the O'Neil et al. 2019 observable based priors, please cite O'Neil et al. 2019.
• If you use RV phase folded plot, please consider citing Makie.jl Danisch & Krumbiegel, (2021).
• If you use the pairplot/cornerplot functionality, please cite:

@misc{Thompson2023,
  author = {William Thompson},
  title = {{PairPlots.jl} Beautiful and flexible visualizations of high dimensional data},
  year = {2023},
  howpublished = {\url{https://sefffal.github.io/PairPlots.jl/dev}},
}

Ready?

Ready to get started? Follow our installation guide and then follow our first tutorial.