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Nonparametric star formation histories (SFHs) have long promised to be the`gold standard' for galaxy spectral energy distribution (SED) modeling as theyare flexible enough to describe the full diversity of SFH shapes, whereasparametric models rule out a significant fraction of these shapes {\it apriori}. However, this flexibility is not fully constrained even withhigh-quality observations, making it critical to choose a well-motivated prior.Here, we use the SED-fitting code \texttt{Prospector} to explore the effect ofdifferent nonparametric priors by fitting SFHs to mock UV-IR photometrygenerated from a diverse set of input SFHs. First, we confirm thatnonparametric SFHs recover input SFHs with less bias and return more accurateerrors than do parametric SFHs. We further find that, while nonparametric SFHsrobustly recover the overall shape of the input SFH, the primary determinant ofthe size and shape of the posterior star formation rate (SFR) as a function oftime is the choice of prior, rather than the photometric noise. As a practicaldemonstration, we fit the UV-IR photometry of $\sim$6000 galaxies from the GAMAsurvey and measure inter-prior scatters in mass (0.1 dex), SFR$_{100\;\mathrm{Myr}}$ (0.8 dex), and mass-weighted ages (0.2 dex), with the blueststar-forming galaxies showing the most sensitivity. An important distinguishingcharacteristic for nonparametric models is the characteristic timescale forchanges in SFR(t). This difference controls whether galaxies are assembled inbursts 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-qualityspectroscopy has the potential to further distinguish between these proposedmodels of SFR(t).

How to Measure Galaxy Star Formation Histories II: Nonparametric Models