A Clipping Method to Mitigate the Impact of Catastrophic Photometric
  Redshift Errors on Weak Lensing Tomography
  We use the mock catalog of galaxies, constructed based on the COSMOS galaxy
catalog including information on photometric redshifts (photo-z) and SED types
of galaxies, in order to study how to define a galaxy subsample suitable for
weak lensing tomography feasible with optical (and NIR) multi-band data. Since
most of 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 posterior likelihood function of redshift estimation
for each galaxies. This clipping method may allow to obtain clean tomographic
redshift bins (here three bins considered) that have almost no overlaps between
different bins, by discarding more than 70% galaxies of ill-defined photo-z's
corresponding to the number densities of remaining galaxies less than 20 per
square arcminutes for a Subaru-type deep survey. Restricting the ranges of
magnitudes and redshifts and/or adding near infrared data help obtain a cleaner
redshift binning. By using the Fisher information matrix formalism, we
propagate photo-z errors into biases in the dark energy equation of state
parameter 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 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 (S/N)
ratios for measuring the angular cross-correlations of galaxies between finner
photo-z bins, finding the higher S/N values for photo-z bins including photo-z
outliers.
