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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

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 نشر من قبل Jorge Andres Villa Velez
 تاريخ النشر 2021
  مجال البحث فيزياء
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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$_rm{Halpha}=1.16pm0.19$ mag and A$_rm{[OIII]}=1.41pm0.22$ 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 $log_{10}rm{(SFR/rm{M}_odot~rm{yr}^{-1})}=log_{10} (rm{L}_{[rm{OIII]}}/rm{ergs~s^{-1}})-(41.20pm0.02)$. Leaving the slope as a free parameter leads to $log_{10}rm{(SFR/rm{M}_odot~rm{yr}^{-1})}=(0.83pm0.06)log_{10}(rm{L}_{[rm{OIII]}}/rm{ergs~s^{-1}})-(34.01pm2.63)$. 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 $logrm{U}approx-2.85$ 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.



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