objects for which reliable shape estimates cannot be found.

For each remaining object, a postage stamp is generated. Ideally, this should identify any neighboring objects and mask them out (by setting their pixel values equal to background noise). Our postage stamp code also identifies objects which are blended by using a friends-of-friends algorithm to find sets of connected pixels that are a certain threshold (typically 2-3$\sigma$ for the stacked images described below) above the background. If there is any overlap between the object of interest and another object within the field of the postage stamp, we consider the source to be excessively blended and exclude it from further analysis.

### Shapelet Decomposition

Shapelets can be an extremely compact representation of an individual image. However, in reality, they are a [*family*]{} of basis functions. There is a characteristic scaling parameter, $\beta$, which represents the width of the Gaussian kernel in the basis function Hermite polynomials: $${\cal B}_{nm}({\bf \theta})\propto
\exp\left(-\frac{\theta_1^2+\theta_2^2}{2\beta^2}\right) \ .$$ In principle, while all values of $\beta$ will yield an orthonormal basis set, some values produce a dramatically faster reconstruction in terms of the number of coefficients required to reach convergence. Moreover, in reality we don’t [*want*]{} to reconstruct all 