pyaneti*

*From the Italian word pianeti, which means planets

email: oscar.barragan_at_physics.ox.ac.uk

Updated November 2021

Paper I

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

Paper II

Written by Barragán O., Aigrain S., Rajpaul V. M., & Zicher N.

Brief description on pyaneti:

• The code runs in python 3.
• Transit fits for single transits.
• Multi-band fits.
• Gaussian Process (GP) and multidimensional GP regressions.
• 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).
• Joint RV + transit fitting.

If you want to see the cool stuff that pyaneti can do check
these papers.

Check pyaneti wiki to learn how to use it

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 this 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}
}

If you also use the new routines of pyaneti (multi-band modelling, single transit modelling, or Gaussian Process regression), please cite also this paper

Barragán,  O.,  Aigrain,  S.,  Rajpaul,  V.  M.,  &  Zicher,  N.,  2022, MNRAS. 509, 866

you can use this bibTeX entry

@ARTICLE{pyaneti2,
       author = {{Barrag{\'a}n}, Oscar and {Aigrain}, Suzanne and {Rajpaul}, Vinesh M. and {Zicher}, Norbert},
        title = "{PYANETI - II. A multidimensional Gaussian process approach to analysing spectroscopic time-series}",
      journal = {\mnras},
     keywords = {methods: numerical, techniques: photometry, techniques: spectroscopy, planets and satellites: general, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2022,
        month = jan,
       volume = {509},
       number = {1},
        pages = {866-883},
          doi = {10.1093/mnras/stab2889},
archivePrefix = {arXiv},
       eprint = {2109.14086},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022MNRAS.509..866B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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 oscar.barragan_at_physics.ox.ac.uk

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!