n this work, we study the properties of the mass density field in the non-Gaussian world models simulated by Grossi et al. In particular, we focus on the one-point density probability distribution function of the mass density field in non-Gaussian models with quadratic non-linearities quantified by the usual parameter fNL. We find that the imprints of primordial non-Gaussianity are well preserved in the negative tail of the probability function during the evolution of the density perturbation. The effect is already notable at redshifts as large as 4 and can be detected out to the present epoch. At z = 0, we find that the fraction of the volume occupied by regions with underdensity δ < -0.9, typical of voids, is about 1.3 per cent in the Gaussian case and increases to ~2.2 per cent if fNL = -1000 while decreases to ~0.5 per cent if fNL = +1000. This result suggests that void-based statistics may provide a powerful method to detect non-Gaussianity even at low redshifts, which is complementary to the measurements of the higher order moments of the probability distribution function like the skewness or the kurtosis for which deviations from the Gaussian case are detected at the 25-50 per cent level.

The mass density field in simulated non-Gaussian scenarios

BRANCHINI, ENZO FRANCO;
2008

Abstract

n this work, we study the properties of the mass density field in the non-Gaussian world models simulated by Grossi et al. In particular, we focus on the one-point density probability distribution function of the mass density field in non-Gaussian models with quadratic non-linearities quantified by the usual parameter fNL. We find that the imprints of primordial non-Gaussianity are well preserved in the negative tail of the probability function during the evolution of the density perturbation. The effect is already notable at redshifts as large as 4 and can be detected out to the present epoch. At z = 0, we find that the fraction of the volume occupied by regions with underdensity δ < -0.9, typical of voids, is about 1.3 per cent in the Gaussian case and increases to ~2.2 per cent if fNL = -1000 while decreases to ~0.5 per cent if fNL = +1000. This result suggests that void-based statistics may provide a powerful method to detect non-Gaussianity even at low redshifts, which is complementary to the measurements of the higher order moments of the probability distribution function like the skewness or the kurtosis for which deviations from the Gaussian case are detected at the 25-50 per cent level.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1072310
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