  We use simulated galaxy observations from the NIHAO-SKIRT-Catalog to test the
accuracy of Spectral Energy Distribution (SED) modeling techniques. SED
modeling is an essential tool for inferring star-formation histories from
nearby galaxy observations, but is fraught with difficulty due to our
incomplete understanding of stellar populations, chemical enrichment processes,
and the nonlinear, geometry-dependent effects of dust on our observations. The
NIHAO-SKIRT-Catalog uses hydrodynamic simulations and radiative transfer to
produce SEDs from the ultraviolet (UV) through the infrared (IR), accounting
for the effects of dust. We use the commonly used Prospector software to
perform inference on these SEDs, and compare the inferred stellar masses and
star-formation rates (SFRs) to the known values in the simulation. We match the
stellar population models to isolate the effects of differences in the
star-formation history, the chemical evolution history, and the dust. We find
that the combined effect of model mismatches for high mass ($> 10^{9.5}
M_{\odot}$) galaxies leads to inferred SFRs that are on average underestimated
by a factor of 2 when fit to UV through IR photometry, and a factor of 3 when
fit to UV through optical photometry. These biases lead to significant
inaccuracies in the resulting sSFR-mass relations, with UV through optical fits
showing particularly strong deviations from the true relation of the simulated
galaxies. In the context of massive existing and upcoming photometric surveys,
these results highlight that star-formation history inference from photometry
remains imprecise and inaccurate, and that there is a pressing need for more
realistic testing of existing techniques.
