tSZ cross-correlations with large-scale structure tracers prefer low S8 and strong baryonic feedback, yielding S8 = 0.72 and low group baryon fraction in FLAMINGO simulations.
Euclid preparation: II. The EuclidEmulator -- A tool to compute the cosmology dependence of the nonlinear matter power spectrum
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abstract
We present a new power spectrum emulator named EuclidEmulator that estimates the nonlinear correction to the linear dark matter power spectrum. It is based on a spectral decomposition method called polynomial chaos expansion. All steps in the construction of the emulator 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 $z\leq 5$. The emulator is constructed using the uncertainty quantification software UQLab and it has been optimized first by creating mock emulators based on Takahashi's HALOFIT. We show that it is possible to successfully predict the performance of the final emulator in this way prior to performing any N-body simulations. We provide a C-code to calculate the nonlinear correction at a relative accuracy of $\sim0.3\%$ with respect to N-body simulations within 50 ms. The absolute accuracy of the final nonlinear power spectrum is comparable to one obtained with N-body simulations, i.e. $\sim 1\%$ for $k\lesssim 1\,h\,{\rm Mpc}^{-1}$ and $z\lesssim 3.5$. This enables efficient forward modeling in the nonlinear regime allowing for maximum likelihood estimation of cosmological parameters. EuclidEmulator has been compared to HALOFIT and CosmicEmu, an alternative emulator based on the Mira-Titan Universe, 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.
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Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
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FLAMINGO: The thermal history of the Universe from tSZ effect cross-correlations and its dependencies on cosmology and baryon physics
tSZ cross-correlations with large-scale structure tracers prefer low S8 and strong baryonic feedback, yielding S8 = 0.72 and low group baryon fraction in FLAMINGO simulations.
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Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.