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The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology
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abstract
We present the BACCO project, a simulation framework specially designed to provide highly-accurate predictions for the distribution of mass, galaxies, and gas as a function of cosmological parameters. In this paper, we describe our main suite of simulations (L $\sim2$ Gpc and $4320^3$ particles) and present various validation tests. Using a cosmology-rescaling technique, we predict the nonlinear mass power spectrum over the redshift range $0<z<1.5$ and over scales $10^{-2} < k/(h Mpc^{-1} ) < 5$ for 800 points in an 8-dimensional cosmological parameter space. For an efficient interpolation of the results, we build an emulator and compare its predictions against several widely-used methods. Over the whole range of scales considered, we expect our predictions to be accurate at the 2\% level for parameters in the minimal $\Lambda$ CDM model and to 3\% when extended to dynamical dark energy and massive neutrinos. We make our emulator publicly available under http://www.dipc.org/bacco
Forward citations
Cited by 3 Pith papers
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Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
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Low-redshift constraints on structure growth from CMB lensing tomography
Low-redshift galaxy clustering and CMB lensing tomography with hybrid effective field theory gives S8=0.79±0.06, consistent with Planck, while data alone prefer Ωm=0.245±0.024.
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Extending CSST Emulator to post-DESI era
A tuned 'spectral equivalence' mapping lets the CSST emulator predict nonlinear matter power spectra at ~1% accuracy across the DESI DR2+CMB dynamic-dark-energy posterior.
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