  We describe a new method for measuring the true redshift distribution of any
set of objects studied only photometrically. The angular cross-correlation
between objects in a photometric sample with objects in some spectroscopic
sample as a function of the spectroscopic z, in combination with standard
correlation measurements, provides sufficient information to reconstruct the
true redshift distribution of the photometric sample. This technique enables
the robust calibration of photometric redshifts even beyond spectroscopic
limits. The spectroscopic sample need not resemble the photometric one in
galaxy properties, but must overlap in sky coverage and redshift range. We test
this new technique with Monte Carlo simulations using realistic error
estimates. RMS errors in recovering both the mean and sigma of the true,
Gaussian redshift distribution of a single photometric redshift bin are
1.4x10^(-3) (sigma_z/0.1) (Sigma_p/10)^(-0.3) (dN_s/dz / 25,000)^(-0.5), where
sigma_z is the true sigma of the redshift distribution, Sigma_p is the surface
density of the photometric sample in galaxies/arcmin^2, and dN_s/dz is the
number of galaxies with a spectroscopic redshift per unit z. We test the impact
of redshift outliers and of a variety of sources of systematic error; none
dominate measurement uncertainties in reasonable scenarios. With this method,
the true redshift distributions of even arbitrarily faint photometric redshift
samples may be determined to the precision required by proposed dark energy
experiments (errors in mean and sigma below 3x10^(-3) at z~1) using expected
extensions of current spectroscopic samples.
