  Broadband photometry of galaxies measures an unresolved mix of complex
stellar populations, gas, and dust. Interpreting these data is a challenge for
models: many studies have shown that properties derived from modeling galaxy
photometry are uncertain by a factor of two or more, and yet answering key
questions in the field now requires higher accuracy than this. Here, we present
a new model framework specifically designed for these complexities. Our model,
Prospector-$\alpha$, includes dust attenuation and re-radiation, a flexible
attenuation curve, nebular emission, stellar metallicity, and a 6-component
nonparametric star formation history. The flexibility and range of the
parameter space, coupled with MCMC sampling within the Prospector inference
framework, is designed to provide unbiased parameters and realistic error bars.
We assess the accuracy of the model with aperture-matched optical spectroscopy,
which was excluded from the fits. We compare spectral features predicted solely
from fits to the broadband photometry to the observed spectral features. Our
model predicts H$\alpha$ luminosities with a scatter of $\sim$0.18 dex and an
offset of $\sim$0.1 dex across a wide range of morphological types and stellar
masses. This agreement is remarkable, as the H$\alpha$ luminosity is dependent
on accurate star formation rates, dust attenuation, and stellar metallicities.
The model also accurately predicts dust-sensitive Balmer decrements,
spectroscopic stellar metallicities, PAH mass fractions, and the age- and
metallicity-sensitive features D$_{\mathrm{n}}$4000 and H$\delta$. Although the
model passes all these tests, we caution that we have not yet assessed its
performance at higher redshift or the accuracy of recovered stellar masses.
