  Galaxies can be described by features of their optical spectra such as oxygen
emission lines, or morphological features such as spiral arms. Although
spectroscopy provides a rich description of the physical processes that govern
galaxy evolution, spectroscopic data are observationally expensive to obtain.
For the first time, we are able to robustly predict galaxy spectra directly
from broad-band imaging. We present a powerful new approach using a hybrid
convolutional neural network with deconvolution instead of batch normalization;
this hybrid CNN outperforms other models in our tests. The learned mapping
between galaxy imaging and spectra will be transformative for future wide-field
surveys, such as with the Vera C. Rubin Observatory and Nancy Grace Roman Space
Telescope, by multiplying the scientific returns for spectroscopically-limited
galaxy samples.
