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Calculate astrophysical false positive probabilities for transiting exoplanet signals

Project description

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Validation of Exoplanet Signals using a Probabilistic Algorithm--- calculating false positive probabilities for transit signals

For usage and more info, `check out the documentation <>`_.


To install, you can get the most recently released version from PyPI::

pip install vespa [--user]

Or you can clone the repository::

git clone
cd vespa
python 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 <>`_, and (in several corners of the code), another package of mine called `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.

Basic Usage

The simplest way to run an FPP calculation straight out of the box is
as follows.

1. 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``).

2. 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

3. 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

maxrad = 12 # aperture radius [arcsec]
secthresh = 1e-4 # Maximum allowed depth of potential secondary eclipse

4. 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::

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 = {},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}


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 = {},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}

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