Mathematics – Logic
Scientific paper
Jan 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21543704m&link_type=abstract
American Astronomical Society, AAS Meeting #215, #437.04; Bulletin of the American Astronomical Society, Vol. 42, p.390
Mathematics
Logic
Scientific paper
Many of the cosmological tests to be performed by planned dark energy experiments will require extremely well-characterized photometric redshift measurements. Current estimates are that the true mean redshift of the objects in each photo-z bin must be known to better than 0.002(1+z) if errors in cosmological measurements are not to be degraded. A conventional approach is to calibrate these photometric redshifts with large sets of spectroscopic redshifts. However, at the depths probed by Stage III surveys (such as DES), let alone Stage IV (LSST, JDEM, Euclid), existing large redshift samples have all been highly (25-60%) incomplete, with a strong dependence of success rate on both redshift and galaxy properties. A powerful alternative approach is to exploit the clustering of galaxies to perform photometric redshift calibrations. Measuring the two-point angular cross-correlation between objects in some photometric redshift bin and objects with known spectroscopic redshift, as a function of the spectroscopic z, allows the true redshift distribution of a photometric sample to be reconstructed in detail, even if it includes objects too faint for spectroscopy or if spectroscopic samples are highly incomplete. We have shown this technique gives good results when tested using mock catalogs designed to match the DEEP2 Galaxy Redshift survey. However, when using standard correlation function estimators, errors are dominated by the variance in the integral constraint, which was not included in previous error budgets and can make the technique weaker if spectroscopic surveys only cover a few square degrees. In this poster, we present new tests of cross-correlation reconstruction using a newer correlation estimator which reduces this effect, yielding an improved reconstruction of redshift distributions.
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