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  • Euclid preparation: II. The...
    Knabenhans, Mischa; Stadel, Joachim; Marelli, Stefano; Potter, Doug; Teyssier, Romain; Legrand, Laurent; Schneider, Aurel; Sudret, Bruno; Blot, Linda; Awan, Saeeda; Burigana, Carlo; Carvalho, Carla Sofia; Kurki-Suonio, Hannu; Sirri, Gabriele

    Monthly notices of the Royal Astronomical Society, 04/2019, Letnik: 484, Številka: 4
    Journal Article

    Abstract We present a new power spectrum emulator named EuclidEmulator that estimates the nonlinear correction to the linear dark matter power spectrum depending on the six cosmological parameters ωb, ωm, ns, h, $w$0, and σ8. It is constructed using the uncertainty quantification software UQLab using a spectral decomposition method called polynomial chaos expansion. All steps in its construction have been tested and optimized: the large high-resolution N-body simulations carried out with PKDGRAV3 were validated using a simulation from the Euclid Flagship campaign and demonstrated to have converged up to wavenumbers $k\approx 5\, h\, {\rm Mpc}^{-1}$ for redshifts $z$ ≤ 5. The emulator is based on 100 input cosmologies simulated in boxes of (1250 Mpc/h)3 using 20483 particles. We show that by creating mock emulators it is possible to successfully predict and optimize the performance of the final emulator prior to performing any N-body simulations. The absolute accuracy of the final nonlinear power spectrum is as good as one obtained with N-body simulations, conservatively, ${\sim } 1$ per cent for $k\lesssim 1\, h\, {\rm Mpc}^{-1}$ and $z$ ≲ 1. This enables efficient forward modelling in the nonlinear regime, allowing for estimation of cosmological parameters using Markov Chain Monte Carlo methods. EuclidEmulator has been compared to HALOFIT, CosmicEmu, and NGenHalofit, and shown to be more accurate than these other approaches. This work paves a new way for optimal construction of future emulators that also consider other cosmological observables, use higher resolution input simulations, and investigate higher dimensional cosmological parameter spaces.