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We use the mock catalog of galaxies, constructed based on the COSMOS galaxycatalog including information on photometric redshifts (photo-z) and SED typesof galaxies, in order to study how to define a galaxy subsample suitable forweak lensing tomography feasible with optical (and NIR) multi-band data. Sincemost of useful cosmological information arises from the sample variance limitedregime for upcoming lensing surveys, a suitable subsample can be obtained bydiscarding a large fraction of galaxies that have less reliable photo-zestimations. We develop a method to efficiently identify photo-z outliers bymonitoring the width of posterior likelihood function of redshift estimationfor each galaxies. This clipping method may allow to obtain clean tomographicredshift bins (here three bins considered) that have almost no overlaps betweendifferent bins, by discarding more than 70% galaxies of ill-defined photo-z'scorresponding to the number densities of remaining galaxies less than 20 persquare arcminutes for a Subaru-type deep survey. Restricting the ranges ofmagnitudes and redshifts and/or adding near infrared data help obtain a cleanerredshift binning. By using the Fisher information matrix formalism, wepropagate photo-z errors into biases in the dark energy equation of stateparameter w. We found that, by discarding most of ill-defined photo-z galaxies,the bias in w can be reduced to the level comparable to the marginalizedstatistical error, however, the residual, small systematic bias remains due toasymmetric scatters around the relation between photometric and true redshifts.We also use the mock catalog to estimate the cumulative signal-to-noise (S/N)ratios for measuring the angular cross-correlations of galaxies between finnerphoto-z bins, finding the higher S/N values for photo-z bins including photo-zoutliers.

A Clipping Method to Mitigate the Impact of Catastrophic Photometric Redshift Errors on Weak Lensing Tomography