Calibrating photometric redshift distributions with cross-correlations
  The next generation of proposed galaxy surveys will increase the number of
galaxies with photometric redshifts by two orders of magnitude, drastically
expanding both redshift range and detection threshold from the current state of
the art. Obtaining spectra for a fair sub-sample of this new data could be
cumbersome and expensive. However, adequate calibration of the true redshift
distribution of galaxies is vital to tapping the potential of these surveys. We
examine a promising alternative to direct spectroscopic follow up: calibration
of the redshift distribution of photometric galaxies via cross-correlation with
an overlapping spectroscopic survey whose members trace the same density field.
We review the theory, develop a pipeline, apply it to mock data from N-body
simulations, and examine the properties of this redshift distribution
estimator. We demonstrate that the method is effective, but the estimator is
weakened by two factors. 1) The correlation function of the spectroscopic
sample must be measured in many bins along the line of sight, rendering it
noisy and interfering with high quality reconstruction of the photometric
redshift distribution. 2) The method is not able to disentangle the photometric
redshift distribution from evolution in the bias of the photometric sample. We
establish the impact of these factors using our mock catalogs. Although it may
still be necessary to spectroscopically follow up a fair subsample of the
photometric survey data, further refinement may appreciably decrease the number
of spectra that will be needed to calibrate future surveys.
