CALIBRATING PHOTOMETRIC REDSHIFT DISTRIBUTIONS WITH CROSS-CORRELATIONS

The next generation of proposed galaxy surveys will increase the number of galaxies with photometric redshift identifications by two orders of magnitude, drastically expanding both the redshift range and detection threshold from the current state of the art. Obtaining spectra for a fair subsample of these 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 to illuminate the processes of galaxy evolution and to constrain the underlying cosmology and growth of structure. We examine here an 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 to implement the method, apply it to mock data from N-body simulations, and examine the properties of this redshift distribution estimator. We demonstrate that the method is generally effective, but the estimator is weakened by two main factors. One is that the correlation function of the spectroscopic sample must be measured in many bins along the line of sight, which renders the measurement noisy and interferes with high-quality reconstruction of the photometric redshift distribution. Also, the method is not able to disentangle the photometric redshift distribution from redshift dependence in the bias of the photometric sample. We establish the impact of these factors using our mock catalogs. We conclude that it may still be necessary to spectroscopically follow up a fair subsample of the photometric survey data. Nonetheless, it is significant that the method has been successfully implemented on mock data, and with further refinement it may appreciably decrease the number of spectra that will be needed to calibrate future surveys.