CEERS Spectroscopic Confirmation of NIRCam-Selected z > 8 Galaxy Candidates with JWST/NIRSpec: Initial Characterization of their Properties (2301.09482v2)

Abstract: We present JWST NIRSpec spectroscopy for 11 galaxy candidates with photometric redshifts of $z\simeq9-13$ and $M_{\rm\,UV} \in[-21,-18]$ newly identified in NIRCam images in the Cosmic Evolution Early Release Science (CEERS) Survey. We confirm emission line redshifts for 7 galaxies at $z=7.762-8.998$ using spectra at $\sim1-5\mu$m either with the NIRSpec prism or its three medium resolution gratings. For $z\simeq9$ photometric candidates, we achieve a high confirmation rate of $\simeq$90\%, which validates the classical dropout selection from NIRCam photometry. No robust emission lines are identified in three galaxy candidates at $z>10$, where the strong [OIII] and H$\beta$ lines would be redshifted beyond the wavelength range observed by NIRSpec, and the Lyman-$\alpha$ continuum break is not detected with the current sensitivity. Compared with HST-selected bright galaxies ($M_{\rm\,UV}\simeq-22$) that are similarly spectroscopically confirmed at $z\gtrsim8$, these NIRCam-selected galaxies are characterized by lower star formation rates (SFR$\simeq4\,M_{\odot}$~yr$^{-1}$) and lower stellar masses ($\simeq10^{8}\,M_{\odot}$), but with higher [OIII]+H$\beta$ equivalent widths ($\simeq$1100$\r{A}$), and elevated production efficiency of ionizing photons ($\log(\xi_{\rm\,ion}/{\rm\,Hz\,erg}^{-1})\simeq25.8$) induced by young stellar populations ($<10$~Myrs) accounting for $\simeq20\%$ of the galaxy mass, highlighting the key contribution of faint galaxies to cosmic reionization. Taking advantage of the homogeneous selection and sensitivity, we also investigate metallicity and ISM conditions with empirical calibrations using the [OIII]/H$\beta$ ratio. We find that galaxies at $z\sim8-9$ have higher SFRs and lower metallicities than galaxies at similar stellar masses at $z\sim2-6$, which is generally consistent with the current galaxy formation and evolution models.