Kavli Affiliate: Yingjie Peng
| First 5 Authors: Zeyu Gao, Yingjie Peng, Kai Wang, Luis C. Ho, Alvio Renzini
| Summary:
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 non-parametric
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 $z sim 6$. 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 $rm D_{rm n}(4000)$ and $rm Hdelta_{rm
A}$ 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.
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