CosmoFit.PlanckLowEELikelihood

class CosmoFit.PlanckLowEELikelihood(cosmology, version='planck2018')

Bases: BaseLikelihood

Planck 2018 low-multipole EE, via its released probability table.

Parameters:

• cosmology – Cosmology model instance. Must be one CAMB can represent.

• version (str, optional) – Dataset version.

Notes

Unlike every other likelihood here, this one has no residuals and no covariance – there is no mean to subtract from. It reports chi2 = -2 log L so that it composes with the rest (JointLikelihood sums chi2, AIC/BIC consume it), but that number is not a sum of squared pulls and should not be read as one. In particular it does not go to zero at a perfect fit: the table’s log-probabilities are normalized densities, so the best achievable value is some finite negative log-likelihood set by the data, not zero.

residuals() therefore returns the difference between the predicted D_l^EE and the table’s own maximum-probability value at each multipole. That is a genuine diagnostic – it says where the model sits relative to the most likely value – but it is not what the chi2 is built from.

__init__(cosmology, version='planck2018')

Parameters:

version (str)

Methods

__init__(cosmology[, version])
chi2()	-2 log L, so this composes with the Gaussian likelihoods it is summed with.
log_likelihood()	Log-likelihood, read straight out of the table.
model()	Predicted D_l^EE = l(l+1) C_l^EE / 2 pi [muK^2] for l = lmin..lmax.
predictions()	Alias for model().
residuals()	Predicted D_l^EE minus the table's most probable value at each multipole.
summary()	Return a summary of the likelihood evaluation.

Attributes

n_data	Number of data points.
name_and_size	Name together with the number of data points.
observable