We compile an updated list of 38 measurements of the Hubble parameter H(z) between redshifts 0.07z2.36 and use them to place constraints on model parameters of constant and time-varying dark energy cosmological models, both spatially flat and curved. We use five models to measure the redshift of the cosmological deceleration-acceleration transition, z da , from these H(z) data. Within the error bars, the measured z da are insensitive to the model used, depending only on the value assumed for the Hubble constant H 0 . The weighted mean of our measurements is z da =0.72±0.05 (0.84±0.03) for H 0 =68±2.8 (73.24±1.74) km sand should provide a reasonably model-independent estimate of this cosmological parameter. The H(z) data are consistent with the standard spatially flat ΛCDM cosmological model but do not rule out nonflat models or dynamical dark energy models.
We present an original phenomenological model to describe the evolution of galaxy number counts, morphologies, and spectral energy distributions across a wide range of redshifts (0.2 < z < 15) and stellar masses [log(M/M ) ≥ 6]. Our model follows observed mass and luminosity functions of both star-forming and quiescent galaxies, and reproduces the redshift evolution of colors, sizes, star-formation and chemical properties of the observed galaxy population. Unlike other existing approaches, our model includes a self-consistent treatment of stellar and photoionized gas emission and dust attenuation based on the BEAGLE tool. The mock galaxy catalogs generated with our new model can be used to simulate and optimize extragalactic surveys with future facilities such as the James Webb Space Telescope (JWST), and to enable critical assessments of analysis procedures, interpretation tools, and measurement systematics for both photometric and spectroscopic data. As a first application of this work, we make predictions for the upcoming JWST Advanced Deep Extragalactic Survey (JADES), a joint program of the JWST/NIRCam and NIRSpec Guaranteed Time Observations teams. We show that JADES will detect, with NIRCam imaging, thousands of galaxies at z 6, and tens at z 10 at m AB 30 (5σ) within the 236 arcmin 2 of the survey. The JADES data will enable accurate constraints on the evolution of the UV luminosity function at z > 8, and resolve the current debate about the rate of evolution of galaxies at z 8. Ready to use mock catalogs and software to generate new realizations are publicly available as the JAdes extraGalactic Ultradeep Artificial Realizations (JAGUAR) package.
Weighted mean and median statistics techniques are used to combine 23 independent lower redshift, z < 1.04, Hubble parameter, H(z), measurements and determine binned forms of H(z). When these are combined with 5 higher redshift, 1.3 z 2.3, H(z) measurements the resulting constraints on cosmological parameters, of three cosmological models, that follow from the weightedmean binned data are almost identical to those derived from analyses using the 28 independent H(z) measurements. This is consistent with what is expected if the lower redshift measurements errors are Gaussian. Plots of the binned weighted-mean H(z)/(1 + z) versus z data are consistent with the presence of a cosmological deceleration-acceleration transition at redshift z da = 0.74 ± 0.05 (Farooq & Ratra 2013b), which is expected in cosmological models with presentepoch energy budget dominated by dark energy as in the standard spatially-flat ΛCDM cosmological model.
We present median statistics central values and ranges for 12 cosmological
parameters, using 582 measurements (published during 1990-2010) collected by
Croft & Dailey (2011). On comparing to the recent Planck collaboration Ade et
al. 2013 estimates of 11 of these parameters, we find good consistency in nine
cases.Comment: 12 pages, 2 figures, 1 tabl
We construct error distributions for a compilation of 232 Large Magellanic Cloud (LMC) distance moduli values from de Grijs et al. (2014) that give an LMC distance modulus of (m − M) 0 = 18.49 ± 0.13 mag (median and 1σ symmetrized error). Central estimates found from weighted mean and median statistics are used to construct the error distributions. The weighted mean error distribution is non-Gaussian -flatter and broader than Gaussian -with more (less) probability in the tails (center) than is predicted by a Gaussian distribution; this could be the consequence of unaccounted-for systematic uncertainties. The median statistics error distribution, which does not make use of the individual measurement errors, is also non-Gaussian -more peaked than Gaussianwith less (more) probability in the tails (center) than is predicted by a Gaussian distribution; this could be the consequence of publication bias and/or the nonindependence of the measurements. We also construct the error distributions of 247 SMC distance moduli values from de Grijs & Bono (2015). We find a central estimate of (m − M) 0 = 18.94 ± 0.14 mag (median and 1σ symmetrized error), and similar probabilities for the error distributions.
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