CONAN._classes.load_lightcurves.limb_darkening

CONAN._classes.load_lightcurves.limb_darkening#

CONAN._classes.load_lightcurves.limb_darkening(q1=0, q2=0, verbose=True)#

Setup Kipping quadratic limb darkening LD coefficient (q1, q2) for transit light curves. Different LD coefficients are required if observations of different filters are used.

Parameters:

  • q1 (float/tuple or list of float/tuple for each filter;) – Stellar quadratic limb darkening coefficients. if tuple, must be of - length 2 for normal prior (mean,std) or length 3 for uniform prior defined as (lo_lim, val, uplim). The values must obey: (0<q1<1) and (0<=q2<1)

  • q2 (float/tuple or list of float/tuple for each filter;) – Stellar quadratic limb darkening coefficients. if tuple, must be of - length 2 for normal prior (mean,std) or length 3 for uniform prior defined as (lo_lim, val, uplim). The values must obey: (0<q1<1) and (0<=q2<1)

_LD_dict#

dictionary of limb darkening parameters for each filter.

Type:

dict;

Examples

# set the limb darkening coefficients for each filter >>> lc_obj.limb_darkening(q1=0.5, q2=0.2) # fixed values for all filters >>> lc_obj.limb_darkening(q1=[0.5,0.6], q2=[0.2,0.3]) # different fixed values for each filter (2 filters)

>>> lc_obj.limb_darkening(q1=(0.5,0.1), q2=(0.2,0.05))  # normal prior for all filters
>>> lc_obj.limb_darkening(q1=[(0.5,0.1),(0.6,0.05)], q2=[(0.2,0.05),(0.3,0.1)])  # different normal prior for each filter (2 filters)

>>> lc_obj.limb_darkening(q1=(0,0.1,1), q2=(0,0.05,1))  # uniform prior for all filters
>>> lc_obj.limb_darkening(q1=[(0,0.1,1),(0.6,0.05,1)], q2=[(0,0.05,1),(0.3,0.1,1)])  # different uniform prior for each filter (2 filters
Contents