  We investigate the impact of photometric signal-to-noise (S/N) on the
precision of photometric redshifts in multi-band imaging surveys, using both
simulations and real data. We simulate the optical 4-band (BVRz) Deep Lens
Survey (DLS, Wittman etal 2002), and use the publicly available Bayesian
Photometric Redshift code BPZ by Benitez (2000). The simulations include a
realistic range of magnitudes and colors and vary from infinite S/N to S/N=5.
The real data are from DLS photometry and two spectroscopic surveys, and
explore a range of S/N by adding noise to initially very high S/N photometry.
Precision degrades steadily as S/N drops, both because of direct S/N effects
and because lower S/N is linked to fainter galaxies with a weaker magnitude
prior. If a simple S/N cut were used, S/N>17 in R (corresponding, in the DLS,
to lower S/N in other bands) would be required to keep the scatter in Deltaz =
(zspec-zphot)/(1+zspec) to less than 0.1. However, cutting on ODDS (a measure
of the peakiness of the probability density function provided by BPZ) greater
than 0.4 provides roughly double the number of usable galaxies with the same
scatter. Ellipticals form the tightest zspec-zphot relation, and cutting on
type=elliptical provides better precision than the ODDS>0.9 cut, but this
eliminates the vast majority of galaxies in a deep survey. In addition to being
more efficient than a type cut, ODDS also has the advantages of working with
all types of galaxies (although ellipticals are overrepresented) and of being a
continuous parameter for which the severity of the cut can be adjusted as
desired.
