Improving Galaxy Cluster Photometric Redshifts

Details Improving Galaxy Cluster Photometric Redshifts Improving Galaxy Cluster Photometric Redshifts

Dec 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006aas...20921503l&link_type=abstract

2007 AAS/AAPT Joint Meeting, American Astronomical Society Meeting 209, #215.03; Bulletin of the American Astronomical Society,

Mathematics

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Scientific paper

The measurement of accurate photometric redshifts (photo-z's) is an important ingredient in enabling optimal scientific analysis of the photometric galaxy cluster samples found in large optical imaging surveys like the Sloan Digital Sky Survey (SDSS) and the proposed Dark Energy Survey (DES). Here we use real SDSS cluster catalogs (taken from both SDSS single-pass and coadded imaging data) and simulated DES cluster samples to examine the accuracy of different photometric redshift estimators. In particular, we show how we can significantly improve upon the original photo-z estimates provided by the maxBCG cluster finding algorithm, by the use of empirical photo-z techniques such as neural network and "nearest-neighbor polynomial", which have been trained on cluster galaxies with spectroscopic redshifts. In addition, we examine the further improvements made possible by stacking the individual photo-z estimates of all the member galaxies of each cluster, and we measure the scaling of the resulting photo-z error with cluster richness, as well as explore the limitations in this method due to systematic errors. Finally, we present estimates of the uncertainties in cluster photometric redshifts for the proposed DES, a 5000 sq. deg. survey which will use a large sample of galaxy clusters out to redshifts z > 1 to help constrain dark energy and cosmological parameters.

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