*From the Italian word pianeti, which means planets
email: [email protected], [email protected]
The newest version works in simililar way to the version 1.0 of pyaneti. You should be able to compile it and run it following this tutorial. But, this new version uses the lapack and blas libraries, be sure you have them, if no, the code may not compile. You should be able to re-run all your scripts of the old pyaneti in this one (But not all the input files for this new pyaneti will run in the old one!).
- Now the code runs in python 3.
- Changes in plots.
- It runs transit fits for single transits.
- It runs multi-band fits.
- It runs GPs and multi-GPs 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).
- Single joint RV + transit fitting.
If you want to see the cool stuff that this new pyaneti can do, check Barragán et al., 2019.
Learn how to install and use pyaneti here
Check pyaneti wiki to learn how to use it!
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}
}
If you also use new routines of pyaneti (multi-band modelling, single transit modelling, or Gaussian Process regression), please cite this paper
Barragán, O., Aigrain, S., Rajpaul, V. M., & Zicher, N., 2022, MNRAS. 509, 866
@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 [email protected]
- 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!