The EXOplanet traNsits and rAdIal veLocity fittER (EXO-NAILER), is an easy-to-use code that allows you to efficiently fit exoplanet transit lightcurves, radial velocities or both.
Author: Néstor Espinoza (nespino@astro.puc.cl)
If you make use of this code, please cite Espinoza et al., 2016, ApJ, in press. (http://arxiv.org/abs/1601.07608)
This code makes use of six important libraries:
- Numpy.
- Scipy.
- The Bad-Ass Transit Model cAlculatioN (batman) for transit modelling (http://astro.uchicago.edu/~kreidberg/batman/).
- emcee for MCMC sampling (http://dan.iel.fm/emcee/current/).
- Astropy for time conversions (http://www.astropy.org).
- The GNU Scientific Library (https://www.gnu.org/software/gsl/)
All of them are open source and can be easily installed in any machine. Be
sure to install them before running the installer (see below), otherwise, it
will complain. This code also makes use of the ajplanet module for
radial-velocity modelling (https://github.com/andres-jordan/ajplanet) and the
flicker-noise module (https://github.com/nespinoza/flicker-noise), for modelling
1/f noise. Copies of the source codes of those modules are included in this repository
and will be installed automatically.
To install the code, simply run the install.py code by doing:
python install.py
After this is done, the code will be ready to use!
To use the code is very simple. Suppose we have a target that we named
my_data:
1. Put the photometry under `transit_data/my_data_lc.dat`. Similarly,
put the RVs (if you have any) under `rv_data/my_data_rvs.dat`. These
are expected to have four columns: times, data, error and name of the
instrument (which is a string); however, only the two first are mandatory:
the code will recognize that you don't have errors on your variables and if no
instrument names are given, it will assume all come from the same instrument.
The flux is expected to be normalized to 1. The RVs are expected to be in km/s.
2. Create a prior file under `priors_data/my_data_priors.dat`. The code
expects this file to have three columns: the parameter name, the prior
type and the hyperparameters of the prior separated by commas (see below).
If you want a parameter to be fixed, put `FIXED` on the Prior Type column
and define the value you want to keep it fixed in the hyperparameters column.
As can be seen from the above, the code can handle data taken with different instruments. Currently this only modifies the outputs of the RVs where, if more than one instrument is detected, a different center-of-mass velocity is fitted for each instrument in order to account for offsets between them, and if jitter is included, a different jitter term is also fitted for each instrument.
Next, you can modify the options in the exonailer.py code. The options are:
target: The name of your target (in this case, `my_data`).
phot_noise_model: This parameter defines the noise model used for the photometry. If set
to 'white', it assumes the underlying noise is white-noise. If set to
'flicker', it assumes it is a white + 1/f.
phot_detrend: This performs a small detrend on the photometry. If set to 'mfilter'
it will median filter and then smooth this filter with a gaussian filter.
It works pretty well for Kepler data. If you don't want to do any kind
of detrending, set this to None.
window: This defines the window of the 'mfilter'. Usually way longer than your
transit event.
phot_get_outliers: This automatically sigma-clips any outliers in your data. It relies on
having decent priors on the ephemeris (t0 and P).
n_omit: Is an array that lets you ommit transit in the fitting procedure (e.g.,
transits with spots). Just put the number of the transits (counted from
the first event in time, with this event counted as 0) that you want
to ommit in the list and the code will do the rest.
ld_law: Limb-darkening law to use. For all the laws but the logarithmic the
sampling is done using the transformations defined in Kipping (2013).
The logarithmic law is sampled according to Espinoza & Jordán (2015b).
mode: Can be set to three different modes. 'full' performs a full transit + rv
fit, 'transit' performs only a transit fit to the photometry, while 'rvs'
performs a fit to the RVs only.
rv_jitter: If set to True, an extra 'jitter' term is added to the RVs error to account
for stellar jitter.
transit_time_def: Defines the input and output time scales (the times are assumed to be in the
JD format, i.e., JD, BJD, MDJ, etc.) of the transit times. If input transit times
are, for example, in utc and you want results in tdb, this has to be 'utc->tdb'.
rv_time_def: Same as for transit times but for the times in the RVs.
Once you are done with this, just run the code by doing:
python exonailer.py
The priors currently supported by the code are:
Normal: Expects that the third column in the prior file has the form
mu,sigma
where mu is the mean value and sigma the standard-deviation.
Uniform: Expects that the third column in the prior file has the form
a,b,
where a is the minimum value and b is the maximum value.
Jeffreys: Expects that the third column in the prior file has the form
low,up
where low is the lower limit of the variable and up is the upper
limit of the variable.
FIXED: This assumes you are giving the fixed value of the variable in
the third column.
The mandatory variables that must have some of the above defined priors are:
Period: The period of the orbit of the exoplanet. Same units as the time.
t0: The time of transit center. Same units as the time.
a: Semi-major axis in stellar units.
p: Planet-to-star radius ratio.
inc: Inclination of the orbit in degrees.
sigma_w: Standard-deviation of the underlying white noise process giving rise to
the observed noise (in ppm).
ecc: Eccentricity of the orbit.
omega: Argument of periapsis (in degrees)
Of course, e.g., for a circular fit, you might want to fix ecc (to 0) and omega (e.g., to 90).
The optional variables are:
mu: Center-of-mass velocity of the RVs.
K: Radial-velocity semi-amplitude.
sigma_w_rv: Jitter term for radial-velocities (see below).
sigma_r: Parameter for 1/f noise (see below).
If in the options of the exonailer.py code you set phot_noise_model to 'flicker', then you
must also define a sigma_r parameter (see Carter & Winn, 2009). If you set rv_jitter to
True, you must also set a sigma_w_rv parameter for the jitter term.
In the case in which you have two or more instruments for the RVs, you can also define
different priors for each value of mu and sigma_w_rv by adding a subscript and the name
of the instrument. For example, if you have data from coralie and feros, instead of
using a common prior for mu and sigma_w_rv for both of them, you can specify one for
each by assigning priors to the variables mu_coralie and sigma_w_rv_coralie and mu_feros
and sigma_w_rv_feros.
+ Add example datasets with RV + transit.
+ Create a tutorial.
+ Add option to add photometry from different instruments.
+ GPs for detrending and for noise models.
+ Transit and RVs for multi-planet systems.
+ Noise models for RVs.