Publications

Matched filter techniques are a staple of modern signal and image processing. They provide a firm foundation (both theoretical and empirical) for detecting and classifying patterns in statistically described backgrounds. Application of these methods to databases has become increasingly common in certain fields (e.g. astronomy). This paper describes an algorithm (based on statistical signal processing methods), a software architecture (based on a hybrid layered approach) and a parallelization scheme (based on a client/server model) for finding clusters in large astronomical databases. The method has proved successful in identifying clusters in real and simulated data. The implementation is flexible and readily executed in parallel on a network of workstations.

We present a comparison of three cluster-finding algorithms from imaging data using Monte Carlo simulations of clusters embedded in a 25 deg(2) region of Sloan Digital Sky Survey (SDSS) imaging data: the matched filter (MF), the adaptive matched filter (AMF), 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 nonuniform 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 (a = -1.1), both algorithms are complete for Abell richness >~ clusters up to z ~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.