Validation of Exoplanet Signals using a Probabilistic Algorithm--- calculating false positive probabilities for transit signals
MIT License
Note: Current recommendation is to retire VESPA in favor of TRICERATOPS (https://github.com/stevengiacalone/triceratops); see research note posted at https://iopscience.iop.org/article/10.3847/2515-5172/acd9a6 for more details.
.. image:: https://travis-ci.com/timothydmorton/VESPA.svg?branch=master :target: https://travis-ci.com/timothydmorton/VESPA
Validation of Exoplanet Signals using a Probabilistic Algorithm--- calculating false positive probabilities for transit signals
For usage and more info, check out the documentation <http://vespa.rtfd.org>
_.
To install, you can get the most recently released version from PyPI::
pip install vespa [--user]
Or you can clone the repository::
git clone https://github.com/timothydmorton/vespa.git
cd vespa
python setup.py install [--user]
The --user
argument may be necessary if you don't have root privileges.
Depends on typical scientific packages (e.g. numpy
, scipy
, pandas
),
as well as isochrones <http://github.com/timothydmorton/isochrones>
, and (in several corners of the code), another package of mine called simpledist <http://github.com/timothydmorton/simpledist>
. All dependencies should get resolved upon install, though this has only been tested under the anaconda Python distribution, which has all the scientific stuff already well-organized.
For best results, it is also recommended to have MultiNest
and pymultinest
installed. Without this, emcee
will be used for stellar modeling, but the MulitNest
results are a bit more trustworthy given the often multi-modal nature of stellar model fitting.
The simplest way to run an FPP calculation straight out of the box is as follows.
Make a text file containing the transit photometry in three columns: t_from_midtransit
[days], flux
[relative, where out-of-transit is normalized to unity], and flux_err
. The file should not have a header row (no titles); and can be either whitespace or comma-delimited (will be ingested by np.loadtxt
).
Make a star.ini
file that contains the observed properties of the target star (photometric and/or spectroscopic, whatever is available)::
#provide spectroscopic properties if available
#Teff = 3503, 80 #value, uncertainty
#feh = 0.09, 0.09
#logg = 4.89, 0.1
#observed magnitudes of target star
# If uncertainty provided, will be used to fit StarModel
J = 9.763, 0.03
H = 9.135, 0.03
K = 8.899, 0.02
Kepler = 12.473
Make a fpp.ini
file containing the following information::
name = k2oi #anything
ra = 11:30:14.510 #can be decimal form too
dec = +07:35:18.21
period = 32.988 #days
rprs = 0.0534 #Rp/Rstar best estimate
photfile = lc_k2oi.csv #contains transit photometry
[constraints]
maxrad = 12 # aperture radius [arcsec]
secthresh = 1e-4 # Maximum allowed depth of potential secondary eclipse
Run the following from the command line (from within the same folder that has star.ini
and fpp.ini
)::
$ calcfpp
Or, if you put the files in a folder called mycandidate
, then you can run calcfpp mycandidate
::
This will run the calculation for you, creating result files, diagnostic plots, etc.
It should take 20-30 minutes. If you want to do a shorter
version to test, you can try calcfpp -n 1000
(the default is 20000). The first
time you run it though, about half the time is doing the stellar modeling, so it will still
take a few minutes.
If you use this code, please cite both the paper and the code.
Paper citation::
@ARTICLE{2012ApJ...761....6M,
author = {{Morton}, T.~D.},
title = "{An Efficient Automated Validation Procedure for Exoplanet Transit Candidates}",
journal = {\apj},
archivePrefix = "arXiv",
eprint = {1206.1568},
primaryClass = "astro-ph.EP",
keywords = {planetary systems, stars: statistics },
year = 2012,
month = dec,
volume = 761,
eid = {6},
pages = {6},
doi = {10.1088/0004-637X/761/1/6},
adsurl = {http://adsabs.harvard.edu/abs/2012ApJ...761....6M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
code::
@MISC{2015ascl.soft03011M,
author = {{Morton}, T.~D.},
title = "{VESPA: False positive probabilities calculator}",
howpublished = {Astrophysics Source Code Library},
year = 2015,
month = mar,
archivePrefix = "ascl",
eprint = {1503.011},
adsurl = {http://adsabs.harvard.edu/abs/2015ascl.soft03011M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}