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Oscar Barragán edited this page Oct 17, 2019 · 8 revisions

WELCOME TO PYANETI WIKI!

Here you will find the steps that you need to install the code in your own computer. As well as some practical examples. Look to the menu bar at the right for basics on how to run pyaneti, examples, theory, etc.

pyaneti

MNRAS arXiv:1809.04609 ascl:1707.003

Written by Barragán O., Gandolfi D. & Antoniciello G.

email: [email protected]

The code

  • Pianeti is the Italian word for planets.
  • Multi-planet fitting of radial velocity and transit data.
  • It uses Markov chain Monte Carlo (MCMC) methods with a Bayesian approach.
  • Ensemble sampler with affine invariance algorithm (Godman & Weare, 2010).
  • Python does the nice things: plots, call functions, printing, in/output files.
  • Fortran does the hard work: MCMC evolution, likelihood calculation, ensemble sampler evolution.
  • Open-source code (GPL v 3.0).

Free and fast code with the robustness of Fortran and the versatility of Python.

Power of pyaneti

  • Multiple independent Markov chains to sample the parameter space.
  • Easy-to-use: it runs by providing only one input_fit.py file.
  • Parallel computing with OpenMP.
  • Automatic creation of posteriors, correlations, and ready-to-publish plots.
  • Circular and eccentric orbits.
  • Multi-planet fitting.
  • Inclusion of RV and photometry jitter.
  • Systemic velocities for multiple instruments.
  • Stellar limb darkening (Mandel & Agol, 2002).
  • Correct treatment of short and long cadence data (Kipping, 2010).
  • Single joint RV + transit fitting.

Learn how to install and use pyaneti here

Citing

If you use pyaneti in your research, please cite it as

Barragán, O., Gandolfi, D., & Antoniciello, G., 2019, MNRAS, 482, 1017

you can use the bibTeX entry

@ARTICLE{pyaneti,
       author = {Barrag\'an, O. and Gandolfi, D. and Antoniciello, G.},
        title = "{PYANETI: a fast and powerful software suite for multiplanet radial
        velocity and transit fitting}",
      journal = {\mnras},
     keywords = {methods: numerical, techniques: photometric, techniques: spectroscopic,
        planets and satellites: general, Astrophysics - Earth and
        Planetary Astrophysics, Astrophysics - Instrumentation and
        Methods for Astrophysics, Physics - Data Analysis, Statistics
        and Probability},
         year = 2019,
        month = Jan,
       volume = {482},
        pages = {1017-1030},
          doi = {10.1093/mnras/sty2472},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/#abs/2019MNRAS.482.1017B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

What will come next?

  • Gaussian process.
  • TTV.
  • Multiband transit photometry fitting.
  • Graphical User Interface.

If you have any comments, requests, suggestions or just need any help, please don't think twice, just contact us!

Warning: This code is under developement and it may contain bugs. If you find something please contact us at [email protected]

Acknowledgements

  • Hannu Parviainen, thank you for helping us to interpret the first result of the PDF of the MCMC chains. We learned a lot from you!
  • Salvador Curiel, thank you for suggestions to parallelize the code.
  • Mabel Valerdi, thank you for being the first pyaneti user, for spotting typos and errors in this document. And thank you much for the awesome idea for pyaneti's logo.
  • Lauren Flor, thank you for testing the code before release.
  • Jorge Prieto-Arranz, thank you for all the suggestions which have helped to improve the code.

THANKS A LOT!

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