Analytical modeling of large-angle CMBR anisotropies from textures.

We propose an analytic method for predicting the large angle CMBR temperature fluctuations induced by model textures. The model makes use of only a small number of phenomenological parameters which ought to be measured from simple simulations. We derive semi-analytically the C l-spectrum for 2 ≤ l ≤ 30 together with its associated non-Gaussian cosmic variance error bars. A slightly tilted spectrum with an extra suppression at low l is found, and we investigate the dependence of the tilt on the parameters of the model. We also produce a prediction for the two point correlation function. We find a high level of cosmic confusion between texture scenarios and standard inflationary theories in any of these quantities. However, we discover that a distinctive non-Gaussian signal ought to be expected at low l, reflecting the prominent effect of the last texture in these multipoles. In the recent past, a large amount of work has been directed towards predicting the CMBR temperature anisotropies associated with inflationary scenarios (see [1] for a review), and with the various topological defect scenarios (e.g. [2, 3]). Inflationary scenarios are far better explored in this respect. The main reason for this is that it is easier in these models to plead ignorance of the detailed mechanism responsible for the density fluctuations of the Universe. Quantum fluctuations in the metric are a fuzzy subject, and hand-waving arguments for Gaussian fluctuations with a particular type of spectrum are normally considered acceptable. Once this is done, one is free to take advantage of the well developed and sophisticated industry of methods aimed at treating Gaussian fluctuations [4]. A precise prediction for the outcome of concrete experiments follows easily, making inflation popular with experimentalists. The state of affairs in topological defect scenarios is rather different. No lack of fundamental physics understanding hampers working out in rigour the defect network evolution and the fluctuations they induce on the matter and radiation of the Universe. The price to pay for our honesty is that a serious treatment of the problem is computationally overpowering. A reliable link between the defect network and the perturbations it induces is undoubtedly still missing. To top off the trouble, it turns out that the fluctuations induced by defects are often non-Gaussian. Setting up a data-analysis framework geared towards non-Gaussian fluctuations is virgin ground (but see [4, 5, 6, 7] for Gaussianity tests). Overall, even though defect scenarios have so …