Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe

By J. Leja, Benjamin D. Johnson, C. Conroy, P. V. van Dokkum, N. Byler. 2016

373 citations recorded by the source corpus.

Broadband photometry of galaxies measures an unresolved mix of complex stellar populations, gas, and dust. Interpreting these data is a challenge for models: many studies have shown that properties derived from modeling galaxy photometry are uncertain by a factor of two or more, and yet answering key questions in the field now requires higher accuracy than this. Here, we present a new model framework specifically designed for these complexities. Our model, Prospector-α, includes dust attenuation and re-radiation, a flexible attenuation curve, nebular emission, stellar metallicity, and a six-component nonparametric star formation history. The flexibility and range of the parameter space, coupled with Monte Carlo Markov chain sampling within the Prospector inference framework, is designed to provide unbiased parameters and realistic error bars. We assess the accuracy of the model with aperture-matched optical spectroscopy, which was excluded from the fits. We compare spectral features predicted solely from fits to the broadband photometry to the observed spectral features. Our model predicts Hα luminosities with a scatter of ∼0.18 dex and an offset of ∼0.1 dex across a wide range of morphological types and stellar masses. This agreement is remarkable, as the Hα luminosity is dependent on accurate star formation rates, dust attenuation, and stellar metallicities. The model also accurately predicts dust-sensitive Balmer decrements, spectroscopic stellar metallicities, polycyclic aromatic hydrocarbon mass fractions, and the age- and metallicity-sensitive features Dn4000 and Hδ. Although the model passes all these tests, we caution that we have not yet assessed its performance at higher redshift or the accuracy of recovered stellar masses.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/960b6c5f9079efc814ab89b88fb9755093c4e5e8

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Citations in the corpus

Dust Attenuation Curves in the Local Universe: Demographics and New Laws for Star-forming Galaxies and High-redshift Analogs (2018, methodology): “…previous efforts to constrain attenuation curves using model-based methods carried out on local galaxies but on smaller scale (Burgarella et al. 2005; Leja et al. 2017), or for larger samples but at higher redshifts (Arnouts et al. 2013; Kriek & Conroy 2013; Salmon et al. 2016; Tress et al. 2018).”
An Older, More Quiescent Universe from Panchromatic SED Fitting of the 3D-HST Survey (2018, methodology): “We use the Prospector inference framework (Johnson & Leja 2017; Leja et al. 2017) to construct a galaxy SED model.”
The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. VII. Properties of the Host Galaxy and Constraints on the Merger Timescale (2017, methodology): “We model the UV to mid-IR SED of NGC 4993 using Prospector-α, a 14-parameter galaxy SED model (Leja et al. 2017a) that is built in the Prospector inference framework (B. Johnson et al. 2017, in preparation) and is optimized to fit UV–IR galaxy broadband photometry.”
Stellar Population Inference with Prospector (2020, background): “Prospector has been in development since 2014 and, in addition to extensive testing on nearby galaxies (Leja et al. 2017, 2018), has seen early use in modeling the SEDs of dwarf galaxies (Pandya et al. 2018; Greco et al. 2018), modeling spatially resolved spectroscopy (Patŕıcio et al. 2018;…”
How to Measure Galaxy Star Formation Histories. II. Nonparametric Models (2018, methodology): “Software: Prospector (Johnson & Leja 2017), python-fsps (Foreman-Mackey et al. 2014), Astropy (Astropy Collaboration et al. 2013, 2018), FSPS (Conroy et al. 2009), matplotlib (Caswell et al. 2018), scipy (Jones et al. 2001–), ipython (Pérez & Granger 2007), numpy (Walt et al. 2011) APPENDIX A.…”
First results from the IllustrisTNG simulations: the galaxy colour bimodality (2017, methodology): “…fibers, identically performed a MCMC fit on both the mock spectral catalog and the actual SDSS spectral sample, using FSPS as the generative model (Leja et al. 2017, John- IllustrisTNG: Galaxy Colors 7 9.0 9.5 10.0 10.5 11.0 11.5 12.0 M ⋆ [ log Msun ] 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 (u -i) c…”
Inferring the star formation histories of massive quiescent galaxies with bagpipes: evidence for multiple quenching mechanisms (2017, methodology): “Recently, Leja et al. (2017) performed tests to validate the Prospector inference framework by comparing physical properties derived from fitting only broad-band photometry against wellcalibrated indicators derived from aperture-matched spectroscopy for a sample of local galaxies.”
Two Remarkably Luminous Galaxy Candidates at z ≈ 10–12 Revealed by JWST (2022, methodology): “Two Luminous z > 10 Galaxy Candidates We confirm the photometric redshifts for the two GLASS candidates and derive stellar population properties using the Prospector SED fitting code (Leja et al. 2017, 2019; Johnson et al. 2021).”