Source code for hierarc.Likelihood.LensLikelihood.td_mag_magnitude_likelihood
import numpy as np
from lenstronomy.Util import constants as const
[docs]
class TDMagMagnitudeLikelihood(object):
"""Likelihood of time delays and magnification likelihood.
This likelihood uses astronomical magnitude units in flux measurement and lensing
magnification and Gaussian uncertainties in this space.
"""
def __init__(
self,
time_delay_measured,
cov_td_measured,
magnitude_measured,
cov_magnitude_measured,
fermat_diff,
magnification_model,
cov_model,
):
"""
:param time_delay_measured: array, relative time delays (relative to the first image) [days]
:param cov_td_measured: 2d array, error covariance matrix of time delay measurement [days^2]
:param magnitude_measured: array, astronomical magnitude of measured fluxes of image positions
:param cov_magnitude_measured: 2d array, error covariance matrix of the measured amplitudes
:param fermat_diff: mean Fermat potential differences (relative to the first image) in arcsec^2
:param magnification_model: mean lensing magnification of the model prediction
in units of astronomical magnitudes (array of length of the images, (mean of - 2.5 * log10(mu))
:param cov_model: 2d array (length relative time delays + image magnifications);
model fermat potential differences and lensing magnification in astronomical magnitudes covariances
"""
self._data_vector = np.append(time_delay_measured, magnitude_measured)
self._cov_td_measured = np.array(cov_td_measured)
self._cov_magnitude_measured = np.array(cov_magnitude_measured)
# check sizes of covariances matches
n_tot = len(self._data_vector)
self._n_td = len(time_delay_measured)
self._n_amp = len(magnitude_measured)
assert self._n_td == len(cov_td_measured)
assert self._n_amp == len(cov_magnitude_measured)
assert n_tot == len(cov_model)
# merge data covariance matrices from time delay and image amplitudes
self._cov_data = np.zeros((n_tot, n_tot))
self._cov_data[: self._n_td, : self._n_td] = self._cov_td_measured
self._cov_data[self._n_td :, self._n_td :] = self._cov_magnitude_measured
# self._fermat_diff = fermat_diff # in units arcsec^2
self._fermat_unit_conversion = (
const.Mpc / const.c / const.day_s * const.arcsec**2
)
# self._mag_model = mag_model
self._model_tot = np.append(fermat_diff, magnification_model)
self._cov_model = cov_model
self.num_data = n_tot
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def log_likelihood(self, ddt, mu_intrinsic):
"""
:param ddt: time-delay distance (physical Mpc)
:param mu_intrinsic: intrinsic brightness of the source (already incorporating the inverse MST transform)
:return: log likelihood of the measured magnified images given the source brightness
"""
model_vector, cov_tot = self._model_cov(ddt, mu_intrinsic)
# invert matrix
try:
cov_tot_inv = np.linalg.inv(cov_tot)
except:
return -np.inf
# difference to data vector
delta = self._data_vector - model_vector
# evaluate likelihood
lnlikelihood = -delta.dot(cov_tot_inv.dot(delta)) / 2.0
sign_det, lndet = np.linalg.slogdet(cov_tot)
lnlikelihood -= 1 / 2.0 * (self.num_data * np.log(2 * np.pi) + lndet)
return lnlikelihood
def _model_cov(self, ddt, mu_intrinsic):
"""Combined covariance matrix of the data and model when marginialized over the
Gaussian model uncertainties in the Fermat potential and magnification.
:param ddt: time-delay distance (physical Mpc)
:param mu_intrinsic: intrinsic brightness of the source (already incorporating
the inverse MST transform)
:return: model vector, combined covariance matrix
"""
# compute model predicted magnified image amplitude and time delay
model_scale = np.append(
ddt * self._fermat_unit_conversion * np.ones(self._n_td),
np.ones(self._n_amp),
)
model_vector = np.zeros_like(self._model_tot)
# time delay prediction
model_vector[: self._n_td] = (
ddt * self._fermat_unit_conversion * self._model_tot[: self._n_td]
)
# lensed astronomical magnitude prediction
model_vector[self._n_td :] = self._model_tot[self._n_td :] + mu_intrinsic
# scale model covariance matrix with model_scale vector (in quadrature),
# shift in astronomical magnitudes do not change covariance matrix
cov_model = model_scale * (self._cov_model * model_scale).T
# combine data and model covariance matrix
cov_tot = self._cov_data + cov_model
return model_vector, cov_tot