Cmb Anisotropy Correlation Function and Topology from Simulated Maps for Map

We have simulated cosmic microwave background (CMB) anisotropy maps for several COBE-DMR-normalized cold dark matter (CDM) cosmogonies, to make predictions for the MAP experiment, an upcoming whole-sky CMB anisotropy space mission. We have studied the sensitivity of the simulated MAP data to cosmology, sky coverage, and instrumental noise. Given an accurate knowledge of instrumental noise, MAP data will ably discriminate amongst the cosmogonies considered, and superbly determine the topology of the initial uctuations. A correlation function analysis of the simulated MAP data results in a very accurate measurement of the acoustic Hubble radius at decoupling. A low-density open CDM model with 0 = 0:4 can be distinguished from the 0 = 1 ducial CDM model or a spatially-at CDM model with a cosmological constant and 0 = 0:4 with more than 99% conndence from the location of the acoustic \valley" in the correlation function. A genus analysis of the simulated MAP data indicates that in cosmogonies with Gaussian random-phase initial conditions, the horizontal shift of the zero-crossing point of the genus curve near the mean temperature threshold level = 0 should not exceed jj = 0:01 (0.04) when the total eeective FWHM smoothing is 0:3 (1:0). The asymmetry of the genus curve at the positive and negative threshold levels should not exceed jg=g(= 1)j = 0:8% (4%) at { 2 { 0:3 (1) FWHM smoothing. Deviations of the observed MAP data in excess of these small values will be evidence for non-Gaussian behavior. The amplitude of the genus curve is a measure of the shape of the power spectrum at the smoothing scale. Even with the expected amount of instrumental noise and partial sky coverage (due to the Galaxy), the MAP data should allow discrimination amongst the cosmogonies considered at more than 99% conndence solely from a genus amplitude analysis. Subject headings: cosmology | cosmic microwave background: anisotropy | large-scale structure of the universe