A clipping method to mitigate the impact of catastrophic photometric redshift errors on weak lensing tomography

Abstract

We use a mock catalog of galaxies based on the COSMOS galaxy catalog, including information on photometric redshift (photo-z) and spectral energy distribution types of galaxies, in order to study how to define a galaxy subsample suitable for weak lensing tomography feasible with optical (and near-IR) multi-band data. Since most useful cosmological information arises from the sample variance limited regime for upcoming lensing surveys, a suitable subsample can be obtained by discarding a large fraction of galaxies that have less reliable photo-z estimations. We develop a method to efficiently identify photo-z outliers by monitoring the width of the posterior likelihood function of redshift estimation for each galaxy. This clipping method may allow us to obtain clean tomographic redshift bins (here three bins are considered) that have almost no overlap, by discarding more than ∼70% of galaxies with ill-defined photo-zs corresponding to the number densities of remaining galaxies less than ∼20 arcmin-2 for a Subaru-type deep survey. Restricting the ranges of magnitudes and redshifts and/or adding near-IR data help us obtain a cleaner redshift binning. Using the Fisher information matrix formalism, we propagate photo-z errors into biases in the dark energy equation of state parameter w. We find that, by discarding most of the ill-defined photo-z galaxies, the bias in w can be reduced to a level comparable to the marginalized statistical error; however, the residual small systematic bias remains due to asymmetric scatters around the relation between photometric and true redshifts. We also use the mock catalog to estimate the cumulative signal-to-noise ratios (S/Ns) for measuring the angular cross-correlations of galaxies between finer photo-z bins, finding higher S/N values for the bins that include photo-z outliers. © 2010. The American Astronomical Society. All rights reserved.