  With current and upcoming experiments such as WFIRST, Euclid and LSST, we can
observe up to billions of galaxies. While such surveys cannot obtain spectra
for all observed galaxies, they produce galaxy magnitudes in color filters.
This data set behaves like a high-dimensional nonlinear surface, an excellent
target for machine learning. In this work, we use a lightcone of semianalytic
galaxies tuned to match CANDELS observations from Lu et al. (2014) to train a
set of neural networks on a set of galaxy physical properties. We add realistic
photometric noise and use trained neural networks to predict stellar masses and
average star formation rates on real CANDELS galaxies, comparing our
predictions to SED fitting results. On semianalytic galaxies, we are nearly
competitive with template-fitting methods, with biases of $0.01$ dex for
stellar mass, $0.09$ dex for star formation rate, and $0.04$ dex for
metallicity. For the observed CANDELS data, our results are consistent with
template fits on the same data at $0.15$ dex bias in $M_{\rm star}$ and $0.61$
dex bias in star formation rate. Some of the bias is driven by SED-fitting
limitations, rather than limitations on the training set, and some is intrinsic
to the neural network method. Further errors are likely caused by differences
in noise properties between the semianalytic catalogs and data. Our results
show that galaxy physical properties can in principle be measured with neural
networks at a competitive degree of accuracy and precision to template-fitting
methods.
