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Weak lensing predictions for coupled dark energy cosmologies at non-linear scales

Authors

  • Emma Beynon,

    Corresponding author
    1. Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth PO1 3FX
      E-mail: emma.beynon@port.ac.uk (EB); marco.baldi@universe-cluster.de (MB)
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  • Marco Baldi,

    Corresponding author
    1. Excellence Cluster Universe, Boltzmannstr. 2, D-85748 Garching, Germany
    2. University Observatory, Ludwig-Maximillians University Munich, Scheinerstr. 1, D-81679 Munich, Germany
      E-mail: emma.beynon@port.ac.uk (EB); marco.baldi@universe-cluster.de (MB)
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  • David J. Bacon,

    1. Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth PO1 3FX
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  • Kazuya Koyama,

    1. Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth PO1 3FX
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  • Cristiano Sabiu

    1. Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT
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E-mail: emma.beynon@port.ac.uk (EB); marco.baldi@universe-cluster.de (MB)

ABSTRACT

We present non-linear weak lensing predictions for coupled dark energy models using the Coupled Dark Energy Cosmological Simulations (CoDECS) simulations. We calculate the shear correlation function and error covariance expected for these models, for forthcoming ground-based [such as Dark Energy Survey (DES)] and space-based (Euclid) weak lensing surveys. We obtain predictions for the discriminatory power of a ground-based survey similar to DES and a space-based survey such as Euclid in distinguishing between Λ cold dark matter (ΛCDM) and coupled dark energy models; we show that using the non-linear lensing signal we could discriminate between ΛCDM and exponential constant coupling models with β0≥ 0.1 at 4σ confidence level with a DES-like survey and β0≥ 0.05 at 5σ confidence level with Euclid. We also demonstrate that estimating the coupled dark energy models’ non-linear power spectrum, using the ΛCDM HALOFIT fitting formula, results in biases in the shear correlation function that exceed the survey errors.

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