juliet¶
juliet
is a versatile modelling tool for transiting and non-transiting exoplanetary
systems that allows to perform quick-and-easy fits to data coming from transit photometry,
radial velocity or both using bayesian inference and, in particular, using Nested Sampling in
order to allow both efficient fitting and proper model comparison.
In this documentation you’ll be able to check out the features juliet
can offer for your
research, which range from fitting different datasets simultaneously for both transits and
radial-velocities to accounting for systematic trends both using linear models or
Gaussian Processes (GP), to even extract information from photometry alone (e.g., stellar rotation
periods) with just a few lines of code.
juliet
builds on the work of “giants” that have made publicly available tools for transit (batman,
starry), radial-velocity (radvel), GP modelling
(george, celerite) and Nested Samplers (MultiNest via
pymultinest, dynesty, ultranest) and thus can be seen as a wrapper of all
of those in one. Somewhat like an Infinity Gauntlet
for exoplanets.
The library is in active development in its public repository on GitHub. If you discover any bugs or have requests for us, please consider sending us an email or opening an issue.
Contributors¶
juliet is being developed by Nestor Espinoza (@nespinoza) and Diana Kossakowski (@dianadianadiana).
Contributions have been made by several authors, including Johannes Buchner (@JohannesBuchner), Jonas Kemmer (@JonasKemmer), Martin Schlecker (@matiscke), Jose Vines (@jvines) and Ian Weaver (@icweaver).
Want to contribute? Grab a project, create your own and open a pull request!
License & Attribution¶
Copyright 2018-2019 Nestor Espinoza & Diana Kossakowski.
juliet is being developed by Nestor Espinoza and Diana Kossakowski in a public GitHub repository. The source code is made available under the terms of the MIT license.
If you make use of this code, please cite the paper:
@ARTICLE{2019MNRAS.490.2262E,
author = {{Espinoza}, N{\'e}stor and {Kossakowski}, Diana and {Brahm}, Rafael},
title = "{juliet: a versatile modelling tool for transiting and non-transiting exoplanetary systems}",
journal = {\mnras},
keywords = {methods: data analysis, methods: statistical, techniques: photometric, techniques: radial velocities, planets and satellites: fundamental parameters, planets and satellites: individual: K2-140b, K2-32b, c, d, Astrophysics - Earth and Planetary Astrophysics},
year = "2019",
month = "Dec",
volume = {490},
number = {2},
pages = {2262-2283},
doi = {10.1093/mnras/stz2688},
archivePrefix = {arXiv},
eprint = {1812.08549},
primaryClass = {astro-ph.EP},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.2262E},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Additional citations¶
In addition to the citation above, and depending on the methods and samplers used in your research, please make sure to cite the appropiate sources:
- If transit fits were performed, cite
batman
:
@ARTICLE{batman,
author = {{Kreidberg}, Laura},
title = "{batman: BAsic Transit Model cAlculatioN in Python}",
journal = {Publications of the Astronomical Society of the Pacific},
keywords = {Astrophysics - Earth and Planetary Astrophysics},
year = 2015,
month = Nov,
volume = {127},
pages = {1161},
doi = {10.1086/683602},
archivePrefix = {arXiv},
eprint = {1507.08285},
primaryClass = {astro-ph.EP},
adsurl = {https://ui.adsabs.harvard.edu/\#abs/2015PASP..127.1161K},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
In addition, juliet
allows to sample limb-darkening coefficients using the method outlined in Kipping (2013). If using it, please cite:
@ARTICLE{2013MNRAS.435.2152K,
author = {{Kipping}, David M.},
title = "{Efficient, uninformative sampling of limb darkening coefficients for two-parameter laws}",
journal = {\mnras},
keywords = {methods: analytical, stars: atmospheres, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics},
year = 2013,
month = nov,
volume = {435},
number = {3},
pages = {2152-2160},
doi = {10.1093/mnras/stt1435},
archivePrefix = {arXiv},
eprint = {1308.0009},
primaryClass = {astro-ph.SR},
adsurl = {https://ui.adsabs.harvard.edu/abs/2013MNRAS.435.2152K},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
If using the uninformative sample for radius and impact parameters outlined in Espinoza (2018), cite:
@ARTICLE{2018RNAAS...2..209E,
author = {{Espinoza}, N{\'e}stor},
title = "{Efficient Joint Sampling of Impact Parameters and Transit Depths in Transiting Exoplanet Light Curves}",
journal = {Research Notes of the American Astronomical Society},
keywords = {Astrophysics - Earth and Planetary Astrophysics},
year = 2018,
month = nov,
volume = {2},
number = {4},
eid = {209},
pages = {209},
doi = {10.3847/2515-5172/aaef38},
archivePrefix = {arXiv},
eprint = {1811.04859},
primaryClass = {astro-ph.EP},
adsurl = {https://ui.adsabs.harvard.edu/abs/2018RNAAS...2..209E},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
- If radial-velocity fits were performed, cite
radvel
:
@ARTICLE{radvel,
author = {{Fulton}, B.~J. and {Petigura}, E.~A. and {Blunt}, S. and {Sinukoff}, E.
},
title = "{RadVel: The Radial Velocity Modeling Toolkit}",
journal = {\pasp},
archivePrefix = "arXiv",
eprint = {1801.01947},
primaryClass = "astro-ph.IM",
year = 2018,
month = apr,
volume = 130,
number = 4,
pages = {044504},
doi = {10.1088/1538-3873/aaaaa8},
adsurl = {http://adsabs.harvard.edu/abs/2018PASP..130d4504F},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
- If Gaussian Processes were used, cite either
george
and/orcelerite
depending on the used kernel(s):
@article{george,
author = {{Ambikasaran}, S. and {Foreman-Mackey}, D. and
{Greengard}, L. and {Hogg}, D.~W. and {O'Neil}, M.},
title = "{Fast Direct Methods for Gaussian Processes}",
year = 2014,
month = mar,
url = http://arxiv.org/abs/1403.6015
}
@article{celerite,
author = {{Foreman-Mackey}, D. and {Agol}, E. and {Angus}, R. and
{Ambikasaran}, S.},
title = {Fast and scalable Gaussian process modeling
with applications to astronomical time series},
year = {2017},
journal = {AJ},
volume = {154},
pages = {220},
doi = {10.3847/1538-3881/aa9332},
url = {https://arxiv.org/abs/1703.09710}
}
- If MultiNest was used to perform the sampling, cite
MultiNest
andPyMultiNest
:
@ARTICLE{MultiNest,
author = {{Feroz}, F. and {Hobson}, M.~P. and {Bridges}, M.},
title = "{MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics}",
journal = {\mnras},
archivePrefix = "arXiv",
eprint = {0809.3437},
keywords = {methods: data analysis , methods: statistical},
year = 2009,
month = oct,
volume = 398,
pages = {1601-1614},
doi = {10.1111/j.1365-2966.2009.14548.x},
adsurl = {http://adsabs.harvard.edu/abs/2009MNRAS.398.1601F},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{PyMultiNest,
author = {{Buchner}, J. and {Georgakakis}, A. and {Nandra}, K. and {Hsu}, L. and
{Rangel}, C. and {Brightman}, M. and {Merloni}, A. and {Salvato}, M. and
{Donley}, J. and {Kocevski}, D.},
title = "{X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue}",
journal = {\aap},
archivePrefix = "arXiv",
eprint = {1402.0004},
primaryClass = "astro-ph.HE",
keywords = {accretion, accretion disks, methods: data analysis, methods: statistical, galaxies: nuclei, X-rays: galaxies, galaxies: high-redshift},
year = 2014,
month = apr,
volume = 564,
eid = {A125},
pages = {A125},
doi = {10.1051/0004-6361/201322971},
adsurl = {http://adsabs.harvard.edu/abs/2014A%26A...564A.125B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
- If dynesty was used to perform the sampling, cite
dynesty
:
@ARTICLE{2020MNRAS.493.3132S,
author = {{Speagle}, Joshua S.},
title = "{DYNESTY: a dynamic nested sampling package for estimating Bayesian posteriors and evidences}",
journal = {\mnras},
keywords = {methods: data analysis, methods: statistical, Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Computation},
year = 2020,
month = apr,
volume = {493},
number = {3},
pages = {3132-3158},
doi = {10.1093/mnras/staa278},
archivePrefix = {arXiv},
eprint = {1904.02180},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.3132S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
- If UltraNest was used to perform the sampling, follow the instructions in the UltraNest read-the-docs.