Fitting spectral energy distributions of FMOS-COSMOS emission-line galaxies at z$\sim$1.6: Star formation rates, dust attenuation, and [OIII]$\lambda$5007 emission-line luminosities

Abstract

We perform SED fitting analysis on a COSMOS sample covering UV-to-FIR wavelengths with emission lines from the FMOS survey. The sample of 182 objects with H$\alpha$ and [OIII]$\lambda5007$ emission spans over a range of $1.40<\rm{z}<1.68$. We obtain robust estimates of stellar mass ($10^{9.5}-10^{11.5}~\rm{M_\odot}$) and SFR ($10^1-10^3~\rm{M_\odot}~\rm{yr}^{-1}$) from the Bayesian analysis with CIGALE fitting continuum photometry and H$\alpha$. We obtain a median attenuation of A$_\rm{H\alpha}=1.16\pm0.19$ mag and A$_\rm{[OIII]}=1.41\pm0.22$ mag. H$\alpha$ and [OIII]$\lambda5007$ attenuations are found to increase with stellar mass, confirming previous findings. A difference of $57$% in the attenuation experienced by emission lines and continuum is found in agreement with the lines being more attenuated than the continuum. New CLOUDY HII-region models in CIGALE enable good fits of H$\alpha$, H$\beta$, [OIII]$\lambda5007$ emission lines with differences smaller than $0.2$ dex. Fitting [NII]$\lambda6584$ line is challenging due to well-known discrepancies in the locus of galaxies in the BPT diagram at intermediate redshifts. We find a positive correlation for SFR and dust-corrected L$_\rm{[OIII]\lambda5007}$ and we derive the linear relation $\log_{10}\rm{(SFR/\rm{M}_\odot~\rm{yr}^{-1})}=\log_{10} (\rm{L}_{[\rm{OIII]}}/\rm{ergs~s^{-1}})-(41.20\pm0.02)$. Leaving the slope as a free parameter leads to $\log_{10}\rm{(SFR/\rm{M}_\odot~\rm{yr}^{-1})}=(0.83\pm0.06)\log_{10}(\rm{L}_{[\rm{OIII]}}/\rm{ergs~s^{-1}})-(34.01\pm2.63)$. Gas-phase metallicity and ionization parameter variations account for a $0.24$ dex and $1.1$ dex of the dispersion, respectively. An average value of $\log\rm{U}\approx-2.85$ is measured for this sample. Including HII-region models to fit simultaneously photometry and emission line fluxes are paramount to analyze future data from surveys such as MOONS and PFS.