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Observations of high-z galaxies and gamma-ray bursts now allow for empirical studies during reionization. However, even deep surveys see only the brightest galaxies at any epoch and must extrapolate to arbitrary lower limits to estimate the total rate of star formation. We first argue that the galaxy populations seen in LBG surveys yield a GRB rate at z > 8 that is an order of magnitude lower than observed. We find that integrating the inferred UV luminosity functions down to M_UV ~ -10 brings LBG- and GRB-inferred SFR density values into agreement up to z ~ 8. GRBs, however, favor a far larger amount of as yet unseen star formation at z > 9. We suggest that the SFR density may only slowly decline out to z ~ 11, in accord with WMAP and Planck reionization results, and that GRBs may be useful in measuring the scale of this multitude of dwarf galaxies.
We present total infrared (IR) and ultraviolet (UV) luminosity functions derived from large representative samples of galaxies at z ~ 0, selected at IR and UV wavelengths from the IRAS IIFSCz catalogue, and the GALEX AIS respectively. We augment thes
We present a linear clustering model of cosmic infrared background (CIB) anisotropies at large scales that is used to measure the cosmic star formation rate density up to redshift 6, the effective bias of the CIB and the mass of dark-matter halos hos
We study the star formation rates (SFRs) of galaxies as a function of local galaxy density at 0.6<z<0.9. We used a low-dispersion prism in IMACS on the 6.5-m Baade (Magellan I) telescope to obtain spectra and measured redshifts to a precision of sigm
We present the star formation rate (SFR) and starburst fraction (SBF) for a sample of field galaxies from the ICBS intermediate-redshift cluster survey. We use [O II] and Spitzer 24 micron fluxes to measure SFRs, and 24 micron fluxes and H-delta abso
We investigate the physics driving the cosmic star formation (SF) history using the more than fifty large, cosmological, hydrodynamical simulations that together comprise the OverWhelmingly Large Simulations (OWLS) project. We systematically vary the