A multi-planet Radial Velocity and Transit fit software
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README.md

pyaneti

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

email: oscaribv@gmail.com
Updated December 09, 2018

MNRAS arXiv:1809.04609 ascl:1707.003 pyaneti wiki

Introduction

  • 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.

Check pyaneti wiki to learn how to use it

Learn ...

  • the basics of pyaneti here here
  • how to run pyaneti in parallel here
  • how to tit a single RV signal with 51 Peg b 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 oscaribv@gmail.com

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!