We present Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation, or B, a new P tool which can be used to rapidly generate complex model galaxy spectra and to fit these to arbitrary combinations of spectroscopic and photometric data using the M N nested sampling algorithm. We extensively test our ability to recover realistic star-formation histories (SFHs) by fitting mock observations of quiescent galaxies from the M simulation. We then perform a detailed analysis of the SFHs of a sample of 9289 quiescent galaxies from UltraVISTA with stellar masses, M * > 10 10 M and redshifts 0.25 < z < 3.75. The majority of our sample exhibit SFHs which rise gradually then quench relatively rapidly, over 1−2 Gyr. This behaviour is consistent with recent cosmological hydrodynamic simulations, where AGN-driven feedback in the low-accretion (jet) mode is the dominant quenching mechanism. At z > 1 we also find a class of objects with SFHs which rise and fall very rapidly, with quenching timescales of < 1 Gyr, consistent with quasar-mode AGN feedback. Finally, at z < 1 we find a population with SFHs which quench more slowly than they rise, over > 3 Gyr, which we speculate to be the result of diminishing overall cosmic gas supply. We confirm the mass-accelerated evolution (downsizing) trend, and a trend towards more rapid quenching at higher stellar masses. However, our results suggest that the latter is a natural consequence of mass-accelerated evolution, rather than a change in quenching physics with stellar mass. We find 61 ± 8 per cent of z > 1.5 massive quenched galaxies undergo significant further evolution by z = 0.5. Bis available at bagpipes.readthedocs.io.

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 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 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 ∼6000 galaxies from the GAMA survey and measure inter-prior scatters in mass (0.1 dex), SFR 100 Myr (0.8 dex), and mass-weighted ages (0.2 dex), with the bluest starforming 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).

Parametric models for galaxy star-formation histories (SFHs) are widely used, though they are known to impose strong priors on physical parameters. This has consequences for measurements of the galaxy stellar-mass function (GSMF), star-formation-rate density (SFRD) and star-forming main sequence (SFMS). We investigate the effects of the exponentially declining, delayed exponentially declining, lognormal and double power law SFH models using Bagpipes. We demonstrate that each of these models imposes strong priors on specific star-formation rates (sSFRs), potentially biasing the SFMS, and also imposes a strong prior preference for young stellar populations. We show that stellar mass, SFR and mass-weighted age inferences from high-quality mock photometry vary with the choice of SFH model by at least 0.1, 0.3 and 0.2 dex respectively. However the biases with respect to the true values depend more on the true SFH shape than the choice of model. We also demonstrate that photometric data cannot discriminate between SFH models, meaning it is important to perform independent tests to find well-motivated priors. We finally fit a low-redshift, volume-complete sample of galaxies from the Galaxy and Mass Assembly (GAMA) Survey with each model. We demonstrate that our stellar masses and SFRs at redshift, z ∼ 0.05 are consistent with other analyses. However, our inferred cosmic SFRDs peak at z ∼ 0.4, approximately 6 Gyr later than direct observations suggest, meaning our mass-weighted ages are significantly underestimated. This makes the use of parametric SFH models for understanding mass assembly in galaxies challenging. In a companion paper we consider non-parametric SFH models.