Python toolkit for calculating stellar limb darkening profiles and model-specific coefficients for arbitrary filters using the stellar spectrum model library by Husser et al (2013).
from ldtk import LDPSetCreator, BoxcarFilter
filters = [BoxcarFilter('a', 450, 550), # Define your passbands
BoxcarFilter('b', 650, 750), # - Boxcar filters useful in
BoxcarFilter('c', 850, 950)] # transmission spectroscopy
sc = LDPSetCreator(teff=(6400, 50), # Define your star, and the code
logg=(4.50, 0.20), # downloads the uncached stellar
z=(0.25, 0.05), # spectra from the Husser et al.
filters=filters) # FTP server automatically.
ps = sc.create_profiles() # Create the limb darkening profiles
cq,eq = ps.coeffs_qd(do_mc=True) # Estimate quadratic law coefficients
lnlike = ps.lnlike_qd([[0.45,0.15], # Calculate the quadratic law log
[0.35,0.10], # likelihood for a set of coefficients
[0.25,0.05]]) # (returns the joint likelihood)
lnlike = ps.lnlike_qd([0.25,0.05],flt=0) # Quad. law log L for the first filter
...and the same, but for 19 narrow passbands...
PyLDTk automates the calculation of custom stellar limb darkening (LD) profiles and model-specific limb darkening coefficients (LDC) using the library of PHOENIX-generated specific intensity spectra by Husser et al. (2013).
The aim of the package is to facilitate exoplanet transit light curve modeling, especially transmission spectroscopy where the modeling is carried out for custom narrow passbands. The package can be
- used to construct model-specific priors on the limb darkening coefficients prior to the transit light curve modeling
- directly integrated into the log posterior computation of any pre-existing transit modeling code with minimal modifications.
The second approach can be used to constrain the LD model parameter space directly by the LD profile, allowing for the marginalization over the whole parameter space that can explain the profile without the need to approximate this constraint by a prior distribution. This is useful when using a high-order limb darkening model where the coefficients are often correlated, and the priors estimated from the tabulated values usually fail to include these correlations.
- Python 2.7
- NumPy => 1.7
- SciPy => 0.14
- IPython => 3.0
Simple: clone the source from github and follow the basic Python package installation routine
git clone https://github.com/hpparvi/ldtk.git
cd ldtk
python setup.py build install [--user]
Examples for basic and more advanced usage can be found from the notebooks
directory.
The LDPSet
class offers methods to calculate log likelihoods for a set of limb darkening models.
lnlike_ln
: Linear modellnlike_qd
: Quadratic modellnlike_nl
: Nonlinear modellnlike_gn
: General model
The limb darkening profiles can be resampled to a desired sampling in mu
using the resampling methods in the LDPSet
.
resample_linear_z(nz=100)
: Resample the profiles to be linear in zresample_linear_mu(nmu=100)
: Resample the profiles to be linear in mureset_sampling()
: Reset back to native sampling in muresample()
:
- LDPSetCreator : Generates a set of limb darkening profiles given a set of filters and stellar TEff, logg, and z.
- LDPSet : Encapsulates the limb darkening profiles and offers methods for model coefficient estimation and log likelihood evaluation.
If you use PyLDTk in your research, please cite the PyLDTk paper
Parviainen, H. & Aigrain, S. MNRAS 453, 3821–3826 (2015) (DOI:10.1093/mnras/stv1857).
and the paper describing the spectrum library without which PyLDTk would be rather useless
Husser, T.-O. et al. A&A 553, A6 (2013) (DOI:10.1051/0004-6361/201219058).
or use these ready made BibTeX entries
@article{Parviainen2015,
author = {Parviainen, Hannu and Aigrain, Suzanne},
doi = {10.1093/mnras/stv1857},
journal = {MNRAS},
month = nov,
number = {4},
pages = {3821--3826},
title = {{ldtk: Limb Darkening Toolkit}},
url = {http://mnras.oxfordjournals.org/lookup/doi/10.1093/mnras/stv1857},
volume = {453},
year = {2015}
}
@article{Husser2013,
author = {Husser, T.-O. and {Wende-von Berg}, S and Dreizler, S and Homeier, D and
Reiners, A and Barman, T. and Hauschildt, Peter H},
doi = {10.1051/0004-6361/201219058},
journal = {A{\&}A},
pages = {A6},
title = {{Astrophysics A new extensive library of PHOENIX stellar atmospheres}},
volume = {553},
year = {2013}
}
Hannu Parviainen, University of Oxford
Ian Crossfield, University of Arizona
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Copyright © 2016 Hannu Parviainen [email protected]