Code to simulate biases introduced by using different limb-darkening laws as explained in Espinoza & Jordán (2016)
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lds_tables
LDC3.py
LICENSE
README.md
Utils.py
run_ld_exosim.py
which_law_should_i_use.py

README.md

ld-exosim

This repository stores codes to (1) select the optimal (i.e. best estimator in a MSE sense) limb-darkening law for a given transiting exoplanet lightcurve and (2) calculate the limb-darkening induced biases on various exoplanet parameters.

The details of the codes are explained in Espinoza & Jordán (2016, MNRAS, in press.; arXiv e-print: http://arxiv.org/abs/1601.05485). Source code of the paper (including generation of all figures): https://github.com/nespinoza/lds_which_law_2_use.

DEPENDENCIES

This code makes use of three important libraries:

+ The Bad-Ass Transit Model cAlculatioN (batman) package: http://astro.uchicago.edu/~kreidberg/batman/
+ The latest version of the lmfit fitter (https://lmfit.github.io/lmfit-py/)
+ The LDC3.py code wrote by David Kipping (https://github.com/davidkipping/LDC3)

This last code might be updated with time, but I have copied here the October 29th, 2015 version of it for reference: be sure to use the latest version of D. Kipping's code!

USAGE

The usage of the code is simple, depending on what you want to do:

  1. Do you want to know which law to use in a given application?

    You are looking for the which_law_should_i_use.py code. Simply modify the parameters inside the code and let the simulations run. At the end, the code will print out the Bias/Precision values for each law so you can select the optimal one for your application.

  2. You want to perform bias simulations for several transit parameters?

    Then you want to use the run_ld_exosim.py code. In the code just define the parameters you woud like to explore and run it. The results will be saved in a folder named "results" for your simulation, where the biases for both fixed and free parameters will be stored.

Both codes make use of limb-darkening tables stored in the ld_tables folder, which already has a table containing all the limb-darkening coefficients using the ATLAS models and the Kepler bandpass. To generate your own table, you can use our code at https://github.com/nespinoza/limb-darkening and put the result inside.