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The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) willproduce several billion photometric redshifts (photo-$z$'s), enablingcosmological analyses to select a subset of galaxies with the most accuratephoto-$z$. We perform initial redshift fits on Subaru Strategic Programgalaxies with deep $grizy$ photometry using Trees for Photo-Z (TPZ) beforeapplying a custom neural network classifier (NNC) tuned to select galaxies with$(z_\mathrm{phot} - z_\mathrm{spec})/(1+z_\mathrm{spec}) < 0.10$. We considerfour cases of training and test sets ranging from an idealized case to usingdata augmentation to increase the representation of dim galaxies in thetraining set. Selections made using the NNC yield significant furtherimprovements in outlier fraction and photo-$z$ scatter ($\sigma_z$) over thosemade with typical photo-$z$ uncertainties. As an example, when selecting thebest third of the galaxy sample, the NNC achieves a 35% improvement in outlierrate and a 23% improvement in $\sigma_z$ compared to using uncertainties fromTPZ. For cosmology and galaxy evolution studies, this method can be tuned toretain a particular sample size or to achieve a desired photo-$z$ accuracy; ourresults show that it is possible to retain more than a third of an LSST-likegalaxy sample while reducing $\sigma_z$ by a factor of two compared to the fullsample, with one-fifth as many photo-$z$ outliers. For surveys like LSST thatare not limited by shot noise, this method enables a larger number oftomographic redshift bins and hence a significant increase in the totalsignal-to-noise of galaxy angular power spectra.

Using a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts