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  • Dark Energy Survey Year 3 R... Dark Energy Survey Year 3 Results: clustering redshifts – calibration of the weak lensing source redshift distributions with redMaGiC and BOSS/eBOSS
    Gatti, M; Giannini, G; Bernstein, G M ... Monthly notices of the Royal Astronomical Society, 02/2022, Letnik: 510, Številka: 1
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    ABSTRACT We present the calibration of the Dark Energy Survey Year 3 (DES Y3) weak lensing (WL) source galaxy redshift distributions n(z) from clustering measurements. In particular, we ...
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43.
  • Imprint of DES superstructu... Imprint of DES superstructures on the cosmic microwave background
    Kovacs, A; Sanchez, C; Garcia-Bellido, J ... Monthly notices of the Royal Astronomical Society, 03/2017, Letnik: 465, Številka: 4
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    Small temperature anisotropies in the cosmic microwave background (CMB) can be sourced by density perturbations via the late-time integrated Sachs-Wolfe (ISW) effect. Large voids and superclusters ...
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44.
  • Birds of a Feather? Magella... Birds of a Feather? Magellan/IMACS Spectroscopy of the Ultra-faint Satellites Grus II, Tucana IV, and Tucana V
    Simon, J. D.; Li, T. S.; Erkal, D. ... The Astrophysical journal, 04/2020, Letnik: 892, Številka: 2
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    Abstract We present Magellan/IMACS spectroscopy of three recently discovered ultra-faint Milky Way satellites, Grus II, Tucana IV, and Tucana V. We measure systemic velocities of , , and for the ...
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45.
  • Finding high-redshift stron... Finding high-redshift strong lenses in DES using convolutional neural networks
    Jacobs, C; Collett, T; Glazebrook, K ... Monthly notices of the Royal Astronomical Society, 04/2019, Letnik: 484, Številka: 4
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    Abstract We search Dark Energy Survey (DES) Year 3 imaging data for galaxy–galaxy strong gravitational lenses using convolutional neural networks. We generate 250 000 simulated lenses at redshifts > ...
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46.
  • Cosmology from cosmic shear... Cosmology from cosmic shear with Dark Energy Survey Science Verification data
    Amara, A.; Annis, J.; Armstrong, R. ... Physical review. D, 07/2016, Letnik: 94, Številka: 2
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    We present the first constraints on cosmology from the Dark Energy Survey (DES), using weak lensing measurements from the preliminary Science Verification (SV) data. We use 139 square degrees of SV ...
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47.
  • The DES Science Verificatio... The DES Science Verification weak lensing shear catalogues
    Jarvis, M; Sheldon, E; Zuntz, J ... Monthly notices of the Royal Astronomical Society, 08/2016, Letnik: 460, Številka: 2
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    We present weak lensing shear catalogues for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey ...
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48.
  • Dark Energy Survey Year 1 r... Dark Energy Survey Year 1 results: constraints on intrinsic alignments and their colour dependence from galaxy clustering and weak lensing
    Samuroff, S; Blazek, J; Troxel, M A ... Monthly notices of the Royal Astronomical Society, 11/2019, Letnik: 489, Številka: 4
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    Abstract We perform a joint analysis of intrinsic alignments and cosmology using tomographic weak lensing, galaxy clustering, and galaxy–galaxy lensing measurements from Year 1 (Y1) of the Dark ...
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49.
  • First cosmology results usi... First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases
    Kessler, R; Brout, D; D’Andrea, C B ... Monthly notices of the Royal Astronomical Society, 05/2019, Letnik: 485, Številka: 1
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    We describe catalog-level simulations of Type Ia Supernova (SN Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics ...
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50.
  • Density split statistics: C... Density split statistics: Cosmological constraints from counts and lensing in cells in DES Y1 and SDSS data
    Gruen, D.; Friedrich, O.; Krause, E. ... Physical review. D, 07/2018, Letnik: 98, Številka: 2
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    We derive cosmological constraints from the probability distribution function (PDF) of evolved large-scale matter density fluctuations. We do this by splitting lines of sight by density based on ...
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