regions. Cutting off these weak connections allowed us to efficiently reduce the bridging of real extended sources with background fluctuations.

Reliability filter and source classification\[classification\]
--------------------------------------------------------------

We applied a reliability criterion by comparing the total photons $C_{total}$ inside the source region to the number of background photons $Bkg$ expected in the same area, as obtained from the background map. We computed the S/N as $C_{net}/\sqrt{Bkg}$, where $C_{net}=C_{total}-Bkg$. This is not the traditional definition of the S/N, which is net counts over the square root of the total counts (source plus background) for Poisson statistics. However, this definition[^6] is a good estimator of the probability of a source to be inconsistent with a Poissonian background fluctuation. In Figure \[poisson\] we show the curves in the $C_{net}-Bkg$ plane that correspond to $S/N = 3,\, 4$ and to a Poissonian probability of $10^{-3}$ and $10^{-4}$ of to be a random fluctuations. A filter for a given reliability threshold can be applied by setting the S/N threshold to the required confidence level. In the remainder of the paper we set this threshold to $S/N > 4$, corresponding to a Poissonian probability of $10^{-4}$ of the signal inside the source region being random fluctuation. We 