Predictions for deep galaxy surveys with JWST from ΛCDM
Abstract
We present predictions for the outcome of deep galaxy surveys with the James Webb Space Telescope (JWST) obtained from a physical model of galaxy formation in Λ cold dark matter. We use the latest version of the GALFORM model, embedded within a new (800 Mpc)3 dark matter only simulation with a halo mass resolution of Mhalo > 2 × 109h-1 M⊙. For computing full UV-to-mm galaxy spectral energy distributions, including the absorption and emission of radiation by dust, we use the spectrophotometric radiative transfer code GRASIL. The model is calibrated to reproduce a broad range of observational data at z ≲ 6, and we show here that it can also predict evolution of the rest-frame far-UV luminosity function for 7 ≲ z ≲ 10 which is in good agreement with observations. We make predictions for the evolution of the luminosity function from z = 16 to z = 0 in all broad-band filters on the Near InfraRed Camera (NIRCam) and Mid InfraRed Instrument (MIRI) on JWST and present the resulting galaxy number counts and redshift distributions. Our fiducial model predicts that ∼1 galaxy per field of view will be observable at z ∼ 11 for a 104 s exposure with NIRCam. A variant model, which produces a higher redshift of reionization in better agreement with Planck data, predicts number densities of observable galaxies ∼5 × greater at this redshift. Similar observations with MIRI are predicted not to detect any galaxies at z ≳ 6. We also make predictions for the effect of different exposure times on the redshift distributions of galaxies observable with JWST, and for the angular sizes of galaxies in JWST bands.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- February 2018
- DOI:
- 10.1093/mnras/stx2897
- arXiv:
- arXiv:1702.02146
- Bibcode:
- 2018MNRAS.474.2352C
- Keywords:
-
- galaxies: evolution;
- galaxies: formation;
- galaxies: high-redshift;
- Astrophysics - Astrophysics of Galaxies
- E-Print:
- 23 Pages, 13 Figures, 4 Tables. Accepted for publication in MNRAS