the model fitting stage by the inclusion of a component of the covariance matrix corresponding to a source model. Scaling this component by a large pre-factor is approximately equivalent to masking out the data at the position of the source. This method is discussed in detail by [@Bond:2000], [@Mason:2003] and [@Sievers:2009]. In the work presented here we consider only simulated mock data and so the presence of strong radio point sources are safely ignored. However in future work we will provide a full description of the point source treatment when presenting actual CBI2 SZ data.

The X-ray Likelihood
--------------------

The raw X-ray surface brightness data are in the form of discrete photon counts distributed over a photon counting detector, and hence will have Poisson distributed errors. However, since we assume that the cluster model is spherically symmetrical, we can radially bin the X-ray surface brightness so that the number of pixels contributing to each bin is typically greater than 100 over the radial range of interest. Under this assumption the likelihood is then well approximated by the Gaussian form given by Equation\[equation:gaussian\_likelihood\]. The errors in each radial bin value are uncorrelated and so the associated covariance is simply given by 