Scale-dependent bias induced by local non-Gaussianity: A comparison to N-body simulations

TL;DR

This study uses N-body simulations to examine how local primordial non-Gaussianity affects the scale-dependent bias in dark matter halo clustering, confirming theoretical predictions on large scales and highlighting deviations on smaller scales.

Contribution

It provides the first detailed comparison between theoretical models and N-body simulations for scale-dependent bias induced by local non-Gaussianity across various halo masses and redshifts.

Findings

Good agreement between theory and simulations for large scales (k<0.03 h/Mpc) with highly biased haloes.

Scale-independent bias correction improves model accuracy on smaller scales.

Bias correction scales linearly with f_NL, consistent with theoretical expectations.

Abstract

We investigate the effect of primordial non-Gaussianity of the local f_NL type on the auto- and cross-power spectrum of dark matter haloes using simulations of the LCDM cosmology. We perform a series of large N-body simulations of both positive and negative f_NL, spanning the range between 10 and 100. Theoretical models predict a scale-dependent bias correction \Delta b(k,f_NL) that depends on the linear halo bias b(M). We measure the power spectra for a range of halo mass and redshifts covering the relevant range of existing galaxy and quasar populations. We show that auto and cross-correlation analyses of bias are consistent with each other. We find that for low wavenumbers with k<0.03 h/Mpc the theory and the simulations agree well with each other for biased haloes with b(M)>1.5. We show that a scale-independent bias correction improves the comparison between theory and simulations on smaller scales, where the scale-dependent effect rapidly becomes negligible. The current limits on f_NL from Slosar et al. (2008) come mostly from very large scales k<0.01 h/Mpc and, therefore, remain valid. For the halo samples with b(M)<1.5-2 we find that the scale- dependent bias from non-Gaussianity actually exceeds the theoretical predictions. Our results are consistent with the bias correction scaling linearly with f_NL.