Galaxy clustering with photometric surveys using PDF redshift
  information
  Photometric surveys produce large-area maps of the galaxy distribution, but
with less accurate redshift information than is obtained from spectroscopic
methods. Modern photometric redshift (photo-z) algorithms use galaxy
magnitudes, or colors, that are obtained through multi-band imaging to produce
a probability density function (PDF) for each galaxy in the map. We used
simulated data to study the effect of using different photo-z estimators to
assign galaxies to redshift bins in order to compare their effects on angular
clustering and galaxy bias measurements. We found that if we use the entire
PDF, rather than a single-point (mean or mode) estimate, the deviations are
less biased, especially when using narrow redshift bins. When the redshift bin
widths are $\Delta z=0.1$, the use of the entire PDF reduces the typical
measurement bias from 5%, when using single point estimates, to 3%.
