Abstract
We use mock galaxy survey simulations designed to resemble the Dark Energy Survey Year 1 (DES Y1) data to validate and inform cosmological parameter estimation. When similar analysis tools are applied to both simulations and real survey data, they provide powerful validation tests of the DES Y1 cosmological analyses presented in companion papers. We use two suites of galaxy simulations produced using different methods, which therefore provide independent tests of our cosmological parameter inference. The cosmological analysis we aim to validate is presented in DES Collaboration et al. (2017) and uses angular two-point correlation functions of galaxy number counts and weak lensing shear, as well as their crosscorrelation, in multiple redshift bins. While our constraints depend on the specific set of simulated realisations available, for both suites of simulations we find that the input cosmology is consistent with the combined constraints from multiple simulated DES Y1 realizations in the Ωm - σ8 plane. For one of the suites, we are able to show with high confidence that any biases in the inferred S8 = σ8(Ωm/0.3)0.5 and Ωm are smaller than the DES Y1 1-σ uncertainties. For the other suite, for which we have fewer realizations, we are unable to be this conclusive; we infer a roughly 60 per cent (70 per cent) probability that systematic bias in the recovered Ωm (S8) is sub-dominant to the DES Y1 uncertainty. As cosmological analyses of this kind become increasingly more precise, validation of parameter inference using survey simulations will be essential to demonstrate robustness.
Original language | English |
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Pages (from-to) | 4614-4635 |
Number of pages | 22 |
Journal | Monthly Notices of the Royal Astronomical Society |
Volume | 480 |
Issue number | 4 |
Early online date | 23 Jul 2018 |
DOIs | |
Publication status | Published - 11 Nov 2018 |
Keywords
- Cosmological parameters
- Large-scale structure of Universe
- RCUK
- STFC
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Avila Perez, S. J. (Creator), D'Andrea, C. B. (Creator) & Thomas, D. (Creator), Oxford University Press, 11 Nov 2018
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