  We present improved methods for using stars found in astronomical exposures
to calibrate both star and galaxy colors as well as to adjust the instrument
flat field. By developing a spectroscopic model for the SDSS stellar locus in
color-color space, synthesizing an expected stellar locus, and simultaneously
solving for all unknown zeropoints when fitting to the instrumental locus, we
increase the calibration accuracy of stellar locus matching. We also use a new
combined technique to estimate improved flat-field models for the Subaru
SuprimeCam camera, forming `star flats' based on the magnitudes of stars
observed in multiple positions or through comparison with available SDSS
magnitudes. These techniques yield galaxy magnitudes with reliable color
calibration (< 0.01 - 0.02 mag accuracy) that enable us to estimate photometric
redshift probability distributions without spectroscopic training samples. We
test the accuracy of our photometric redshifts using spectroscopic redshifts
z_s for ~5000 galaxies in 27 cluster fields with at least five bands of
photometry, as well as galaxies in the COSMOS field, finding sigma((z_p -
z_s)/(1 + z_s)) ~ 0.03 for the most probable redshift z_p. We show that the
full posterior probability distributions for the redshifts of galaxies with
five-band photometry exhibit good agreement with redshifts estimated from
thirty-band photometry in the COSMOS field. The growth of shear with increasing
distance behind each galaxy cluster shows the expected redshift-distance
relation for a flat Lambda-CDM cosmology. Photometric redshifts and calibrated
colors are used in subsequent papers to measure the masses of 51 galaxy
clusters from their weak gravitational shear. We make our Python code for
stellar locus matching available at http://big-macs-calibrate.googlecode.com;
the code requires only a catalog and filter functions.
