Using a Neural Network Classifier to Select Galaxies with the Most
  Accurate Photometric Redshifts
  The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will
produce several billion photometric redshifts (photo-$z$'s), enabling
cosmological analyses to select a subset of galaxies with the most accurate
photo-$z$. We perform initial redshift fits on Subaru Strategic Program
galaxies with deep $grizy$ photometry using Trees for Photo-Z (TPZ) before
applying a custom neural network classifier (NNC) tuned to select galaxies with
$(z_\mathrm{phot} - z_\mathrm{spec})/(1+z_\mathrm{spec}) < 0.10$. We consider
four cases of training and test sets ranging from an idealized case to using
data augmentation to increase the representation of dim galaxies in the
training set. Selections made using the NNC yield significant further
improvements in outlier fraction and photo-$z$ scatter ($\sigma_z$) over those
made with typical photo-$z$ uncertainties. As an example, when selecting the
best third of the galaxy sample, the NNC achieves a 35% improvement in outlier
rate and a 23% improvement in $\sigma_z$ compared to using uncertainties from
TPZ. For cosmology and galaxy evolution studies, this method can be tuned to
retain a particular sample size or to achieve a desired photo-$z$ accuracy; our
results show that it is possible to retain more than a third of an LSST-like
galaxy sample while reducing $\sigma_z$ by a factor of two compared to the full
sample, with one-fifth as many photo-$z$ outliers. For surveys like LSST that
are not limited by shot noise, this method enables a larger number of
tomographic redshift bins and hence a significant increase in the total
signal-to-noise of galaxy angular power spectra.
