We present a comparison of three cluster finding algorithms from imaging data using Monte Carlo simulations of clusters embedded in a 25 \mbox { deg } ^ { 2 } region of Sloan Digital Sky Survey ( SDSS ) imaging data : the Matched Filter ( MF ; Postman et al . 1996 ) , the Adaptive Matched Filter ( AMF ; Kepner et al . 1999 ) and a color-magnitude filtered Voronoi Tessellation Technique ( VTT ) .
Among the two matched filters , we find that the MF is more efficient in detecting faint clusters , whereas the AMF evaluates the redshifts and richnesses more accurately , therefore suggesting a hybrid method ( HMF ) that combines the two .
The HMF outperforms the VTT when using a background that is uniform , but it is more sensitive to the presence of a non-uniform galaxy background than is the VTT ; this is due to the assumption of a uniform background in the HMF model .
We thus find that for the detection thresholds we determine to be appropriate for the SDSS data , the performance of both algorithms are similar ; we present the selection function for each method evaluated with these thresholds as a function of redshift and richness .
For simulated clusters generated with a Schechter luminosity function ( M _ { r } ^ { * } = -21.5 and \alpha = -1.1 ) both algorithms are complete for Abell richness \buildrel > \over { \sim } 1 clusters up to z \sim 0.4 for a sample magnitude limited to r = 21 .
While the cluster parameter evaluation shows a mild correlation with the local background density , the detection efficiency is not significantly affected by the background fluctuations , unlike previous shallower surveys .