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Strategies for Obtaining Robust Spectral Energy Distribution Fitting Parameters for Galaxies at z ∼ 1 and z ∼ 2 in the Absence of Infrared Data
Robust estimation of star formation rates (SFRs) at higher redshifts ( z ≳ 1) using UV–optical–near-infrared (NIR) photometry is contingent on the ability of spectral energy distribution (SED) fitting to constrain the dust attenuation, stellar metallicity, and star formation history (SFH) simultaneo...
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Published in: | The Astrophysical journal 2024-02, Vol.962 (1), p.59 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Robust estimation of star formation rates (SFRs) at higher redshifts (
z
≳ 1) using UV–optical–near-infrared (NIR) photometry is contingent on the ability of spectral energy distribution (SED) fitting to constrain the dust attenuation, stellar metallicity, and star formation history (SFH) simultaneously. IR-derived dust luminosities can help break the degeneracy between these parameters, but IR data are often not available. Here, we explore strategies for SED fitting at
z
≳ 1 in the absence of IR data using a sample of log
M
*
> 10.2 star-forming galaxies from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) for which 24
μ
m data are available. We adopt the total IR luminosity (
L
TIR
) obtained from 24
μ
m as the “ground truth,” which allows us to assess how well it can be recovered (as
L
dust
) from UV–optical–NIR SED fitting. We test a variety of dust attenuation models, stellar population synthesis models, metallicity assumptions, and SFHs separately to identify which assumptions maximize the agreement (correlation and linearity) between
L
TIR
and
L
dust
. We find that a flexible dust attenuation law performs best. For stellar populations, we find that Bruzual & Charlot models are favored over those of Eldridge et al. Fixing the stellar metallicity at solar value is preferred to other fixed values or leaving it as a free parameter. For SFHs, we find that minimizing the variability in the recent (
8
and 0.7 <
z
< 1.3, obtained using the models we found to be the most robust. |
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ISSN: | 0004-637X 1538-4357 |
DOI: | 10.3847/1538-4357/ad17c8 |