https://github.com/adrn/exoplanet

Fast & scalable MCMC for all your exoplanet needs!

Science Score: 10.0%

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Low similarity (11.4%) to scientific vocabulary

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Repository

Fast & scalable MCMC for all your exoplanet needs!

Basic Info

Host: GitHub
Owner: adrn
License: mit
Default Branch: master
Homepage: https://docs.exoplanet.codes
Size: 74.5 MB

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Fork of exoplanet-dev/exoplanet

Created about 6 years ago · Last pushed about 6 years ago

https://github.com/adrn/exoplanet/blob/master/

# exoplanet


  
    
  
    
  
    
  
    
  
    
  

  
    
  
    
  
    
  
    
  

  
    
  
    
  
    
  
    
  
    


_exoplanet_ is a toolkit for probabilistic modeling of transit and/or
radial velocity observations of
[exoplanets](https://en.wikipedia.org/wiki/Exoplanet) and other
astronomical time series using [PyMC3](https://docs.pymc.io). _PyMC3_ is
a flexible and high-performance model building language and inference
engine that scales well to problems with a large number of parameters.
_exoplanet_ extends _PyMC3_'s language to support many of the custom
functions and distributions required when fitting exoplanet datasets.
These features include:

- A fast and robust solver for Kepler's equation.
- Scalable Gaussian Processes using
  [celerite](https://celerite.readthedocs.io).
- Fast and accurate limb darkened light curves using
  [starry](https://rodluger.github.io/starry).
- Common reparameterizations for [limb darkening
  parameters](https://arxiv.org/abs/1308.0009), and [planet radius and
  impact parameter](https://arxiv.org/abs/1811.04859).
- And many others!

All of these functions and distributions include methods for efficiently
calculating their _gradients_ so that they can be used with
gradient-based inference methods like [Hamiltonian Monte
Carlo](https://arxiv.org/abs/1206.1901), [No U-Turns
Sampling](https://arxiv.org/abs/1111.4246), and [variational
inference](https://arxiv.org/abs/1603.00788). These methods tend to be
more robust than the methods more commonly used in astronomy (like
[ensemble samplers](https://emcee.readthedocs.io) and [nested
sampling](https://ccpforge.cse.rl.ac.uk/gf/project/multinest/))
especially when the model has more than a few parameters. For many
exoplanet applications, _exoplanet_ (the code) can improve the typical
performance by orders of magnitude.

_exoplanet_ is being actively developed in [a public repository on
GitHub](https://github.com/exoplanet-dev/exoplanet) so if you have any trouble,
[open an issue](https://github.com/exoplanet-dev/exoplanet/issues) there.