NormalLogLike

class paintbox.NormalLogLike(observed, model, obserr=None, mask=None)

Bases: paintbox.likelihoods.LogLike

Normal loglikelihood for SED modeling.

The normal log-likelihood is given by

\begin{equation} \ln \mathcal{L}(y, \sigma|\theta)= -\frac{N}{2}\ln (2\pi) -\frac{1}{2}\sum_{i=1}^N \left (\frac{f(\theta)- y_i}{\sigma_i} \right )^2 - \frac{1}{2}\sum_{i=1}^{N}\ln \sigma_i^2 \end{equation}

where \(y\) is the observed spectrum, \(\sigma\) are the uncertainties, \(\theta\) is the input vector of parameters and and \(f(\theta)\) is the SED model.

Parameters

observed: numpy.ndarray

Observed spectro-photometric SED of object.

model: paintbox SED model

SED model used in the modelling.

obserr: numpy.ndarray, optional

Uncertainties in the observed SED fitting to be used in the weighting of the log-likelihood.

mask: numpy.ndarray, optional

Mask for observed data, with zeros (ones) indicating non-masked ( masked) wavelengths.

Attributes

parnames: list

List with name of variables used in the evaluation of the log-likelihood.

Methods Summary

__call__(theta)	Calculation of the log-likelihood.
gradient(theta)	Gradient of the log-likelihood.

Methods Documentation

__call__(theta)

Calculation of the log-likelihood.

gradient(theta)

Gradient of the log-likelihood.