How to Measure Galaxy Star Formation Histories. II. Nonparametric Models

By J. Leja, A. Carnall, Benjamin D. Johnson, C. Conroy, Joshua S. Speagle. Astrophysical Journal, 2018

342 citations recorded by the source corpus.

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 as a function of time (SFR(t)) 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 Galaxy and Mass Assembly survey and measure scatters between priors to be 0.1 dex in mass, 0.8 dex in SFR100 Myr, and 0.2 dex in mass-weighted ages, with the bluest star-forming 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).

Subfields: Astronomy and Astrophysics, Ecology, Instrumentation

View Paper: https://www.semanticscholar.org/paper/dfc9ac7b4652c86376127c320df7c0af86bf7e88

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

1. P. V. van Dokkum
2. M. Franx
3. I. Labbé
4. P. Oesch
5. R. Bouwens
6. G. Illingworth
7. C. Conroy
8. Richard S. Ellis

In Ecology

1. Benjamin D. Johnson
2. J. Leja
3. C. Conroy
4. Joshua S. Speagle

In Instrumentation

1. P. V. van Dokkum
2. M. Franx
3. I. Labbé
4. P. Oesch
5. G. Illingworth
6. R. Bouwens
7. C. Conroy
8. Richard S. Ellis

Citations in the corpus

• A JWST/NIRSpec First Census of Broad-line AGNs at z = 4–7: Detection of 10 Faint AGNs with M BH ∼ 106–108 M ⊙ and Their Host Galaxy Properties (2023, background): “2 dex (Leja et al. 2019), which is smaller than the statistical uncer-Table tainty.”
• Stellar Population Inference with Prospector (2020, background): “In the often highly degenerate space of SED modeling, priors can play a strong role in determining the shape of the posterior probability distribution (e.g. Carnall et al. 2019a; Leja et al. 2019).”
• An Older, More Quiescent Universe from Panchromatic SED Fitting of the 3D-HST Survey (2018, methodology): “Classic grid-based models grow exponentially in size with model dimensionality, but gridless “on-the-fly” models combined with Markov chain Monte Carlo (MCMC) algorithms can efficiently explore highdimensional (N7) spaces (Chevallard & Charlot 2016; Leja et al. 2017; Carnall et al. 2018).”
• 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).”