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arXiv · astro-ph/9711057

CMB Anisotropy Correlation Function and Topology from Simulated Maps for MAP

Abstract

We have simulated cosmic microwave background (CMB) anisotropy maps for several COBE-DMR-normalized cold dark matter (CDM) cosmogonies, to make predictions for the upcoming MAP experiment. We have studied the sensitivity of the simulated MAP data to cosmology, sky coverage, and instrumental noise. With accurate knowledge of instrumental noise, MAP data will discriminate among the cosmogonies considered, and determine the topology of the initial fluctuations. 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 Omega_0=0.4 can be distinguished from the Omega_0=1 fiducial CDM model or a Lambda CDM model with > 99% confidence 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, a shift of the zero-crossing point of the genus curve near the mean temperature threshold level nu=0 should not exceed |Delta nu| = 0.01 (0.04) when the total effective FWHM smoothing is 0.3 deg (1.0 deg). The asymmetry of the genus curve at the positive and negative threshold levels should not exceed |Delta g/g(nu=1,-1)| = 0.8% (4%) at 0.3 deg (1.0 deg) 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% confidence solely from a genus amplitude analysis.

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BibTeXRIS

Changbom Park, Wesley N. Colley, J. Richard Gott III, Bharat Ratra, David N. Spergel, Naoshi Sugiyama. 1997-11-06. CMB Anisotropy Correlation Function and Topology from Simulated Maps for MAP. https://doi.org/10.1086/306259

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