A Bayesian Population Model for the Observed Dust Attenuation in
  Galaxies
  Dust plays a pivotal role in determining the observed spectral energy
distribution (SED) of galaxies. Yet our understanding of dust attenuation is
limited and our observations suffer from the dust-metallicity-age degeneracy in
SED fitting (single galaxies), large individual variances (ensemble
measurements), and the difficulty in properly dealing with uncertainties
(statistical considerations). In this study, we create a population Bayesian
model to rigorously account for correlated variables and non-Gaussian error
distributions and demonstrate the improvement over a simple Bayesian model. We
employ a flexible 5-D linear interpolation model for the parameters that
control dust attenuation curves as a function of stellar mass, star formation
rate (SFR), metallicity, redshift, and inclination. Our setup allows us to
determine the complex relationships between dust attenuation and these galaxy
properties simultaneously. Using Prospector fits of nearly 30,000 3D-HST
galaxies, we find that the attenuation slope ($n$) flattens with increasing
optical depth ($\tau$), though less so than in previous studies. $\tau$
increases strongly with SFR, though when $\log~{\rm SFR}\lesssim 0$, $\tau$
remains roughly constant over a wide range of stellar masses. Edge-on galaxies
tend to have larger $\tau$ than face-on galaxies, but only for $\log~M_*\gtrsim
10$, reflecting the lack of triaxiality for low-mass galaxies. Redshift
evolution of dust attenuation is strongest for low-mass, low-SFR galaxies, with
higher optical depths but flatter curves at high redshift. Finally, $n$ has a
complex relationship with stellar mass, highlighting the intricacies of the
star-dust geometry. We have publicly released software
(https://github.com/Astropianist/DustE) for users to access our population
model.
