From Halos to Galaxies. X. Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction - Zeyu Gao; Yingjie Peng; Kai Wang; Luis C. Ho; Alvio Renzini; Anna R. Gallazzi; Filippo Mannucci; Houjun Mo; Yipeng Jing; Xiaohu Yang; Enci Wang; Dingyi Zhao; et al. (The Astrophysical Journal)
The spectral energy distribution (SED) of galaxies is essential for deriving fundamental properties like stellar mass and star formation history (SFH). However, conventional methods, including both parametric and nonparametric approaches, often fail to accurately recover the observed cosmic star formation rate (SFR) density due to oversimplified or unrealistic assumptions about SFH and their inability to account for the complex SFH variations across different galaxy populations. To address this issue, we introduce a novel approach that improves galaxy broadband SED analysis by incorporating physical priors derived from hydrodynamical simulations. Tests using IllustrisTNG simulations demonstrate that our method can reliably determine galaxy physical properties from broadband photometry, including stellar mass within 0.05 dex, current SFR within 0.3 dex, and fractional stellar formation time within 0.2 dex, with a negligible fraction of catastrophic failures. When applied to the Sloan Digital Sky Survey (SDSS) main photometric galaxy sample with spectroscopic redshift, our estimates of stellar mass and SFR are consistent with the widely used MPA-JHU and GSWLC catalogs. Notably, using the derived SFHs of individual SDSS galaxies, we estimate the cosmic SFR density and stellar mass density with remarkable consistency to direct observations up to z  ~ 6. This demonstrates a significant advancement in deriving SFHs from SEDs that closely align with observational data. Consequently, our method can reliably recover observed spectral indices such as D _n (4000) and H δ _A by synthesizing the full spectra of galaxies using the estimated SFHs and metal enrichment histories, relying solely on broadband photometry as input. Furthermore, this method is extremely computationally efficient compared to conventional approaches.