Optical Cluster-Finding with an Adaptive Matched-Filter Technique: Algorithm and Comparison with Simulations
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
- Research Organization:
- Stanford Linear Accelerator Center (SLAC)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC02-76SF00515
- OSTI ID:
- 918970
- Report Number(s):
- SLAC-PUB-12810; arXiv:0709.0759
- Journal Information:
- Submitted to Astrophysical Journal, Journal Name: Submitted to Astrophysical Journal
- Country of Publication:
- United States
- Language:
- English
Similar Records
The C4 clustering algorithm: Clusters of galaxies in the Sloan Digital Sky Survey
AN OPTICAL CATALOG OF GALAXY CLUSTERS OBTAINED FROM AN ADAPTIVE MATCHED FILTER FINDER APPLIED TO SLOAN DIGITAL SKY SURVEY DATA RELEASE 6