Impact of scale-dependent bias and nonlinear evolution on the ISW

TL;DR

This paper investigates how nonlinear evolution and scale-dependent bias affect the ISW effect in the CMB, using simulations and perturbation theory, and finds that these factors do not significantly alter the detectability of the ISW signal.

Contribution

It provides a detailed analysis of nonlinear and scale-dependent bias effects on the ISW signal using N-body simulations and perturbation theory, clarifying their impact on observations.

Findings

Nonlinear evolution enhances the small-scale ISW signal at late times.

Perturbation theory accurately predicts the nonlinear effects for k<0.2 Mpc/h.

Nonlinearity and bias scale dependence do not significantly affect the ISW signal-to-noise ratio.

Abstract

I summarize recent results from Smith, Hernandez-Monteagudo & Seljak (2009), a study of the impact of nonlinear evolution of gravitational potentials in the LCDM model on the Integrated Sachs-Wolfe (ISW) contribution to the cross-power spectrum of the CMB and a set of biased tracers of the mass. We use a large ensemble of N-body simulations to directly follow the potentials and compare the results to analytic perturbation theory (PT) methods. The PT predictions match our results to high precision for k<0.2 Mpc/h. We analyze the CMB-density tracer cross-spectrum using simulations and renormalized bias PT, and find good agreement. The usual assumption is that nonlinear evolution enhances the growth of structure and counteracts the linear ISW on small scales, leading to a change in sign of the CMB-LSS cross-spectrum at small scales. However, PT analysis suggests that this trend reverses at late times when the logarithmic growth rate f=d ln D/d ln a<1/2 or Omega_m (z)<0.3. Numerical results confirm these expectations and we find nonlinear enhancement of the ISW signal on small scales at late-times. On computing the total contribution to the angular spectrum, we find that nonlinearity and scale dependence of the bias are unable to influence the signal-to-noise of the current and future measurements.