How to Measure Galaxy Star Formation Histories II: Nonparametric Models (1811.03637v2)

Abstract: 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 {\it 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 \texttt{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 $\sim$6000 galaxies from the GAMA survey and measure inter-prior scatters in mass (0.1 dex), SFR$_{100\; \mathrm{Myr}}$ (0.8 dex), and mass-weighted ages (0.2 dex), 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).

• The paper demonstrates that nonparametric models better capture diverse star formation histories compared to traditional parametric methods.
• It utilizes the Prospector SED-fitting code with mock UV-IR photometry to show how prior selection heavily influences derived star formation rates.
• The study highlights that nonparametric methods yield less biased estimates and more accurate error assessments in galaxy evolution analyses.

Nonparametric Models for Galaxy Star Formation Histories: An Analytical Exploration

In the paper of galaxy formation, understanding the star formation history (SFH) of a galaxy is pivotal. This paper, authored by Leja et al., explores nonparametric models for deriving SFHs from galaxy spectral energy distributions (SEDs), proposing them as a potentially superior alternative to traditional parametric approaches. It emphasizes the flexibility of nonparametric models in capturing the diverse and complex nature of SFHs without being constrained by predefined functional forms.

Key Contributions and Methodologies

1. Nonparametric Model Flexibility: Unlike parametric models that bound SFHs within certain pre-assumed shapes (e.g., exponential decline), nonparametric models accommodate a broader spectrum of SFH configurations. This research harnesses the utility of the Prospector SED-fitting code to examine the influence of various nonparametric priors on SFHs using mock UV-IR photometric data.
2. Intrinsic Constraints of Priors: The investigation underscores the importance of the choice of prior. It identifies that priors, rather than photometric noise, predominantly shape the posterior distributions of star formation rates (SFRs) over time. This sensitivity introduces a responsibility to select priors thoughtfully to avoid biases, especially with high-quality observations.
3. Comparison with Parametric Models: Through methodical analysis, the paper shows nonparametric SFHs provide less biased and more accurate error estimates compared to parametric models. This advantage arises from the flexibility of nonparametric models to mimic complex, real-world SFH shapes seen in simulations—something parametric models struggle with due to their restrictive functional foundations.
4. Practical Demonstration and Real-World Application: The application of these methodologies to data from the Galaxy and Mass Assembly (GAMA) survey underscores the practical benefits of nonparametric SFHs. The research presents empirical scatter differences between priors in terms of galaxy mass, recent star formation activity, and mass-weighted ages.

Implications and Future Speculations

The findings emphasize the nontrivial impact of prior selection on derived galaxy evolution parameters. The flexibility of nonparametric models presents a compelling case for their preference, particularly in contexts where the observational data might not strongly constrain SFHs. By capturing a wide array of potential SFH realizations, nonparametric models provide robust and adaptable frameworks that align theoretical expectations from complex hydrodynamical simulations with observational data.

The paper speculates on future paths to synergy between observations and simulations, suggesting that tunable nonparametric priors could offer refined insights into galaxy formation dynamics. As high-fidelity photometric and spectroscopic datasets become increasingly accessible, these models could evolve to harness novel observational metrics, including emission line analysis, to further disentangle the intricate tapestry of galaxy evolution.

Conclusion

This paper represents a significant advancement in the way astronomers approach galaxy SED modeling. By demonstrating the efficacy and superior adaptability of nonparametric SFH models, it encourages a more nuanced utilization of priors tailored to empirical and theoretical frameworks. As astronomical observations increasingly demand precision and breadth, nonparametric methods may well lead future explorations of galaxy formation histories, providing a cohesive link between simulations and observation.