How to Measure Galaxy Star Formation Histories II: Nonparametric Models
  Nonparametric star formation histories (SFHs) have long promised to be the
`gold standard' for galaxy spectral energy distribution (SED) modeling as they
are flexible enough to describe the full diversity of SFH shapes, whereas
parametric models rule out a significant fraction of these shapes {\it a
priori}. However, this flexibility is not fully constrained even with
high-quality observations, making it critical to choose a well-motivated prior.
Here, we use the SED-fitting code \texttt{Prospector} to explore the effect of
different nonparametric priors by fitting SFHs to mock UV-IR photometry
generated from a diverse set of input SFHs. First, we confirm that
nonparametric SFHs recover input SFHs with less bias and return more accurate
errors than do parametric SFHs. We further find that, while nonparametric SFHs
robustly recover the overall shape of the input SFH, the primary determinant of
the size and shape of the posterior star formation rate (SFR) as a function of
time is the choice of prior, rather than the photometric noise. As a practical
demonstration, we fit the UV-IR photometry of $\sim$6000 galaxies from the GAMA
survey and measure inter-prior scatters in mass (0.1 dex), SFR$_{100\;
\mathrm{Myr}}$ (0.8 dex), and mass-weighted ages (0.2 dex), with the bluest
star-forming galaxies showing the most sensitivity. An important distinguishing
characteristic for nonparametric models is the characteristic timescale for
changes in SFR(t). This difference controls whether galaxies are assembled in
bursts or in steady-state star formation, corresponding respectively to
(feedback-dominated/accretion-dominated) models of galaxy formation and to
(larger/smaller) confidence intervals derived from SED-fitting. High-quality
spectroscopy has the potential to further distinguish between these proposed
models of SFR(t).
