from hierarc.LensPosterior.kin_constraints import KinConstraints
[docs]
class DdtGaussKinConstraints(KinConstraints):
"""Class for sampling Ds/Dds posteriors from imaging data and kinematic constraints
with additional constraints on the time-delay distance Ddt."""
def __init__(
self,
z_lens,
z_source,
ddt_mean,
ddt_sigma,
theta_E,
theta_E_error,
gamma,
gamma_error,
r_eff,
r_eff_error,
sigma_v_measured,
kwargs_aperture,
kwargs_seeing,
kwargs_numerics_galkin,
anisotropy_model,
sigma_v_error_independent=None,
sigma_v_error_covariant=None,
sigma_v_error_cov_matrix=None,
kwargs_lens_light=None,
lens_light_model_list=["HERNQUIST"],
MGE_light=False,
kwargs_mge_light=None,
hernquist_approx=True,
kappa_ext=0,
kappa_ext_sigma=0,
sampling_number=1000,
num_psf_sampling=100,
num_kin_sampling=1000,
multi_observations=False,
):
"""
:param z_lens: lens redshift
:param z_source: source redshift
:param ddt_mean: mean of time-delay distance likelihood
:param ddt_sigma: 1-sigma Gaussian uncertainty on the time-delay distance likelihood
:param sigma_v_measured: numpy array of IFU velocity dispersion of the main deflector in km/s
:param sigma_v_error_independent: numpy array of 1-sigma uncertainty in velocity dispersion of the IFU
observation independent of each other
:param sigma_v_error_covariant: covariant error in the measured kinematics shared among all IFU measurements
:param sigma_v_error_cov_matrix: error covariance matrix in the sigma_v measurements (km/s)^2
:type sigma_v_error_cov_matrix: nxn matrix with n the length of the sigma_v_measured array
:param kwargs_aperture: spectroscopic aperture keyword arguments, see lenstronomy.Galkin.aperture for options
:param kwargs_seeing: seeing condition of spectroscopic observation, corresponds to kwargs_psf in the GalKin module specified in lenstronomy.GalKin.psf
:param theta_E: Einstein radius (in arc seconds)
:param theta_E_error: 1-sigma error on Einstein radius
:param gamma: power-law slope (2 = isothermal) estimated from imaging data
:param gamma_error: 1-sigma uncertainty on power-law slope
:param r_eff: half-light radius of the deflector (arc seconds)
:param r_eff_error: uncertainty on half-light radius
:param kwargs_numerics_galkin: numerical settings for the integrated line-of-sight velocity dispersion
:param anisotropy_model: type of stellar anisotropy model. See details in MamonLokasAnisotropy() class of lenstronomy.GalKin.anisotropy
:param kwargs_lens_light: keyword argument list of lens light model (optional)
:param kwargs_mge_light: keyword arguments that go into the MGE decomposition routine
:param hernquist_approx: bool, if True, uses the Hernquist approximation for the light profile
:param kappa_ext: mean of the external convergence from which the ddt constraints are coming from
:param kappa_ext_sigma: 1-sigma distribution uncertainty from which the ddt constraints are coming from
:param multi_observations: bool, if True, interprets kwargs_aperture and kwargs_seeing as lists of multiple
observations
"""
self._ddt_mean, self._ddt_sigma = ddt_mean, ddt_sigma
self._kappa_ext_mean, self._kappa_ext_sigma = kappa_ext, kappa_ext_sigma
super(DdtGaussKinConstraints, self).__init__(
z_lens,
z_source,
theta_E,
theta_E_error,
gamma,
gamma_error,
r_eff,
r_eff_error,
sigma_v_measured,
kwargs_aperture,
kwargs_seeing,
kwargs_numerics_galkin,
anisotropy_model,
sigma_v_error_independent=sigma_v_error_independent,
sigma_v_error_covariant=sigma_v_error_covariant,
sigma_v_error_cov_matrix=sigma_v_error_cov_matrix,
kwargs_lens_light=kwargs_lens_light,
lens_light_model_list=lens_light_model_list,
MGE_light=MGE_light,
kwargs_mge_light=kwargs_mge_light,
hernquist_approx=hernquist_approx,
sampling_number=sampling_number,
num_psf_sampling=num_psf_sampling,
num_kin_sampling=num_kin_sampling,
multi_observations=multi_observations,
)
[docs]
def hierarchy_configuration(self, num_sample_model=20):
"""Routine to configure the likelihood to be used in the hierarchical sampling.
In particular, a default configuration is set to compute the Gaussian
approximation of Ds/Dds by sampling the posterior and the estimate of the
variance of the sample. The anisotropy scaling is then performed. Different
anisotropy models are supported.
:param num_sample_model: number of samples drawn from the lens and light model
posterior to compute the dimensionless kinematic component J()
:return: keyword arguments
"""
j_model_list, error_cov_j_sqrt = self.model_marginalization(num_sample_model)
ani_scaling_array_list = self.anisotropy_scaling()
error_cov_measurement = self.error_cov_measurement
# configuration keyword arguments for the hierarchical sampling
kwargs_likelihood = {
"z_lens": self._z_lens,
"z_source": self._z_source,
"likelihood_type": "DdtGaussKin",
"ddt_mean": self._ddt_mean,
"ddt_sigma": self._ddt_sigma,
"sigma_v_measurement": self._sigma_v_measured,
"anisotropy_model": self._anisotropy_model,
"j_model": j_model_list,
"error_cov_measurement": error_cov_measurement,
"error_cov_j_sqrt": error_cov_j_sqrt,
"ani_param_array": self.ani_param_array,
"ani_scaling_array_list": ani_scaling_array_list,
}
return kwargs_likelihood