with respect to components of the model parameter vector :math:`\boldsymbol{q}`. :math:`\boldsymbol{q}` comprises parameters describing the shape of the model and the model pixel coordinates :math:`\boldsymbol{x}`.
The first term in :eq:`loss_func` is a modified `weighted sum of squares <http://en.wikipedia.org/wiki/Least_squares#Weighted_least_squares>`_ that aims at minimizing the residuals of the fit. :math:`p_i`, :math:`\hat{m}_i(\boldsymbol{q})` and :math:`\sigma_i` are respectively the pixel value above the background, the value of the resampled model, and the pixel value uncertainty at image pixel :math:`i`.
:math:`g(u)` is a derivable monotonous function that reduces the influence of large deviations from the model (e.g., contamination by neighbors):
The vector :math:`\hat{\boldsymbol{m}}(\boldsymbol{q})` is obtained by convolving the high resolution model :math:`\boldsymbol{m}(\boldsymbol{q})` with the local PSF model :math:`\boldsymbol{\phi}` and applying a resampling operator :math:`\mathbf{R}(\boldsymbol{x})` to generate the final model raster at position :math:`\boldsymbol{x}` at the nominal image resolution:
Levenberg-Marquardt minimization, inside a disk which diameter is scaled to include the isophotal footprint plus a 20 % margin, plus the size of the PSF model image.