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Details

Autor(en) / Beteiligte
Titel
The Dark Energy Survey Year 3 high-redshift sample: selection, characterization, and analysis of galaxy clustering
Ist Teil von
  • Monthly notices of the Royal Astronomical Society, 2023-09, Vol.525 (3), p.3896-3922
Ort / Verlag
United States: Oxford University Press
Erscheinungsjahr
2023
Quelle
EZB Free E-Journals
Beschreibungen/Notizen
  • ABSTRACT The fiducial cosmological analyses of imaging surveys like DES typically probe the Universe at redshifts z < 1. We present the selection and characterization of high-redshift galaxy samples using DES Year 3 data, and the analysis of their galaxy clustering measurements. In particular, we use galaxies that are fainter than those used in the previous DES Year 3 analyses and a Bayesian redshift scheme to define three tomographic bins with mean redshifts around z ∼ 0.9, 1.2, and 1.5, which extend the redshift coverage of the fiducial DES Year 3 analysis. These samples contain a total of about 9 million galaxies, and their galaxy density is more than 2 times higher than those in the DES Year 3 fiducial case. We characterize the redshift uncertainties of the samples, including the usage of various spectroscopic and high-quality redshift samples, and we develop a machine-learning method to correct for correlations between galaxy density and survey observing conditions. The analysis of galaxy clustering measurements, with a total signal to noise S/N ∼ 70 after scale cuts, yields robust cosmological constraints on a combination of the fraction of matter in the Universe Ωm and the Hubble parameter h, $\Omega _m h = 0.195^{+0.023}_{-0.018}$, and 2–3  per cent measurements of the amplitude of the galaxy clustering signals, probing galaxy bias and the amplitude of matter fluctuations, bσ8. A companion paper (in preparation) will present the cross-correlations of these high-z samples with cosmic microwave background lensing from Planck and South Pole Telescope, and the cosmological analysis of those measurements in combination with the galaxy clustering presented in this work.
Sprache
Englisch
Identifikatoren
ISSN: 0035-8711
eISSN: 1365-2966
DOI: 10.1093/mnras/stad2402
Titel-ID: cdi_osti_scitechconnect_1998812

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