arcunique / exotransit

Python package for retrieval of properties of exoplanets by model-fitting their transit light curves using MCMC with additional features such as detrending of light curves, GP regression, and continuous monitoring of the retrieval process.

ExoTransit

ExoTransit is a Python package for retrieval of the properties of an exoplanet by modeling its transit light curves using Markov chain Monte Carlo technique on the basis of the transit model of Mandel & Agol (2002) and their transit light curve routines. This package also allows users to simultaneous model the correlated noise with Gaussian process (GP) regression as well as detrend the light curves by choosing arbitrary trend functions and arguments.

One of the greatest features of the package is real-time visualization of the retrieval process by monitoring the progress of walkers, dynamic corner plots, and auto-correlation time of the samples over the iterations. This allows users to control the process manually or opt for automatic completion by choosing a convergence criterion, or the maximum number of iterations.

Author

• Aritra Chakrabarty (IIA, Bangalore)

Requirements

• python>3.6
• numpy
• matplotlib
• scipy
• emcee
• george

Instructions on installation and use

Presently, the code is only available on Github. Either download the code or use the following line on terminal to install using pip:
pip install git+https://github.com/arcunique/ExoTransit #installs from the current master on this repo.

To use the retrieval function use the model_transit_lightcurve class defined. It can be imported by

from ExoTransit.retrieve import model_transit_lightcurve

Documentation of this package is underway. Some example Jupyter notebooks can be found in the run2learn directory of this package which demonstrate how to use the classes and functions. This package has already been used to perform modeling of transit light curves of some hot Jupiters, results from which can be found to be demonstrated in here.