Python package for the K2 systematics correction using Gaussian processes.
git clone https://github.com/OxES/k2sc.git
cd k2sc
python setup.py install --user
A MAST K2 light curve can be detrended by calling
k2sc <filename> -c <c> --splits xxxx,yyyy --flux-type pdc
where <filename>
is the MAST light curve filename, <c>
is the campaign number, and xxxx,yyyy
define the break points (in time) where the position-dependent systematics change (corresponds to the points of reversal of the roll-angle variation).
--de-max-time <ss>
maximum time (in seconds) to run global GP hyperparameter optimization (differential evolution) before switching to local optimization.--de-npop <nn>
size of the de population, can be set to 50 to speed up the optimization.--save-dir <path>
defines where to save the detrended files--logfile <filename>
K2SC supports MPI automatically (requires MPI4Py.) Call k2sc as
mpirun -n N k2sc <files> -c <c> --splits xxxx,yyyy --flux-type pdc
where <files>
is now a list of files to be detrended (for example, path/to/ktwo*.fits
).
- NumPy
- SciPy
- astropy
- George
- MPI4Py
If you use K2SC in your reserach, please cite
Aigrain, S., Parviainen, H. & Pope, B. (2016, accepted to MNRAS), arXiv:1603.09167
or use this ready-made BibTeX entry
@article{Aigrain2016,
arxivId = {1603.09167},
author = {Aigrain, Suzanne and Parviainen, Hannu and Pope, Benjamin},
keywords = {data analysis,methods,photometry,planetary systems,techniques},
title = {{K2SC: Flexible systematics correction and detrending of K2 light curves using Gaussian Process regression}},
url = {http://arxiv.org/abs/1603.09167},
year = {2016}
}
- Hannu Parviainen
- Suzanne Aigrain
- Benjamin Pope