Combining patch-level neural summaries with power spectrum and bispectrum extracts roughly as much cosmological information from dark matter simulations as wavelet statistics, apparently nearing the information limit at 7.8 Mpc/h resolution.
Nonlinear evolution of cosmological power spectra
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abstract
Hamilton et al. have suggested an invaluable scaling formula which describes how the power spectra of density fluctuations evolve into the nonlinear regime of hierarchical clustering. This paper presents an extension of their method to low-density universes and universes with nonzero cosmological constant. We pay particular attention to models with large negative spectral indices, and give a spectrum-dependent fitting formula which is of significantly improved accuracy by comparison with an earlier version of this work. The tendency of nonlinear effects to increase power on small scales is stronger for spectra with more negative spectral indices, and for lower densities. However, for low-density models with a cosmological constant, the nonlinear effects are less strong than for an open universe of the same $\Omega$.
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PatchNet: A hierarchical approach for neural field-level inference from Quijote Simulations
Combining patch-level neural summaries with power spectrum and bispectrum extracts roughly as much cosmological information from dark matter simulations as wavelet statistics, apparently nearing the information limit at 7.8 Mpc/h resolution.