How Well Can We Measure Galaxy Dust Attenuation Curves? The Impact of the Assumed Star-dust Geometry Model in Spectral Energy Distribution Fitting

et al. 2022

ApJ

Dust attenuation varies substantially from galaxy to galaxy and as of yet cannot be reproduced from first principles in theoretical models. In Nagaraj et al., we developed the first Bayesian population model of dust attenuation as a function of stellar population properties and projected galaxy shape, built on spectral energy distribution fits of nearly 30,000 galaxies in the 3D-HST grism survey with broadband photometric coverage from the rest-frame UV to IR. In this paper, we apply the model, named “DustE,” to galaxies from the large-volume cosmological simulation TNG100 at z = 1. We produce a UVJ diagram and compare it with one obtained in previous work by applying approximate radiative transfer to the simulated galaxies. We find that the UVJ diagram based on our empirical model is in better agreement with observations than the previous effort, especially in the number density of dusty star-forming galaxies. We also construct the intrinsic dust-free UVJ diagram for TNG100 and 3D-HST galaxies at z ∼ 1, finding qualitative agreement but residual differences at the 10%–20% level. These differences may be caused by the finding that TNG100 galaxies have, on average, 29% younger stellar populations and possibly higher metallicities than observed galaxies.

Section: Discussionsupporting

confidence: 60%

Section: Dust-free Color Comparisonmentioning

confidence: 92%

Section: Dust-free Color Comparisonmentioning

confidence: 98%

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Empirical Dust Attenuation Model Leads to More Realistic UVJ Diagram for TNG100 Galaxies

Nagaraj

Forbes

et al. 2022

ApJ

“…Relevant to this project: differing ages, metallicities, and dust attenuation curves can produce similar effects in spectra, causing degeneracies (e.g., Conroy 2013;Santini et al 2015). In addition, Lower et al (2022) found that without FIR data, even though the bias introduced is negligible, dust attenuation curves of simulated galaxies are not as well constrained. As the photometry available for SED fitting is typically UV-NIR and possibly MIR, this leads to large uncertainties on attenuation parameters.…”

Section: Introductionmentioning

confidence: 97%

A Bayesian Population Model for the Observed Dust Attenuation in Galaxies

Nagaraj

Forbes

et al. 2022

ApJ

Self Cite

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 5D 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–Hubble Space Telescope galaxies, we find that the attenuation slope (n) flattens with increasing optical depth (τ), though less so than in previous studies. τ increases strongly with SFR, though when log ( SFR ) ≲ 0 , τ remains roughly constant over a wide range of stellar masses. Edge-on galaxies tend to have larger τ than face-on galaxies, but only for log ( M * ) ≳ 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 for users to access our population model.

“…One of the most advanced models in this family is Prospector-α (Leja et al 2017). It incorporates galaxy properties including nonparametric star formation histories (SFHs; Leja et al 2019), selfconsistent nebular emission (Byler et al 2017), and a sophisticated dust model (e.g., Lower et al 2022), and critically, fits all parameters at once with Markov Chain Monte Carlo or nested sampling to produce joint high-dimensional constraints. Prospector-α has been designed specifically to extract the most information from high signal-to-noise photometry or spectra with good wavelength coverage.…”

Section: Introductionmentioning

confidence: 99%

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

Wang

Bezanson

et al. 2023

ApJL

Self Cite

The advent of the James Webb Space Telescope (JWST) signals a new era in exploring galaxies in the high-z universe. Current and upcoming JWST imaging will potentially detect galaxies at z ∼ 20, creating a new urgency in the quest to infer accurate photometric redshifts (photo-z) for individual galaxies from their spectral energy distributions, as well as masses, ages, and star formation rates. Here we illustrate the utility of informed priors encoding previous observations of galaxies across cosmic time in achieving these goals. We construct three joint priors encoding empirical constraints of redshifts, masses, and star formation histories in the galaxy population within the Prospector Bayesian inference framework. In contrast with uniform priors, our model breaks an age–mass–redshift degeneracy, and thus reduces the mean bias error in masses from 0.3 to 0.1 dex, and in ages from 0.6 to 0.2 dex in tests done on mock JWST observations. Notably, our model recovers redshifts at least as accurately as the state-of-the-art photo-z code EAzY in deep JWST fields, but with two advantages: tailoring a model based on a particular survey is rendered mostly unnecessary given well-motivated priors; obtaining joint posteriors describing stellar, active galactic nuclei, gas, and dust contributions becomes possible. We can now confidently use the joint distribution to propagate full non-Gaussian redshift uncertainties into inferred properties of the galaxy population. This model, “Prospector-β,” is intended for fitting galaxy photometry where the redshift is unknown, and will be instrumental in ensuring the maximum science return from forthcoming photometric surveys with JWST. The code is made publicly available online as a part of Prospector 9 9 The version used in this work corresponds to the state of the Git repository at commit https://github.com/bd-j/prospector/commit/820ad72363a1f9c22cf03610bfe6e361213385cd. .