Searching for Signatures of Cosmic Superstrings in the CMB

Because cosmic superstrings generically form junctions and gauge theoretic strings typically do not, junctions may provide a signature to distinguish between cosmic superstrings and gauge theoretic cosmic strings. In cosmic microwave background anisotropy maps, cosmic strings lead to distinctive line discontinuities. String junctions lead to junctions in these line discontinuities. In turn, edge detection algorithms such as the Canny algorithm can be used to search for signatures of strings in anisotropy maps. We apply the Canny algorithm to simulated maps which contain the effects of cosmic strings with and without string junctions. The Canny algorithm produces edge maps. To distinguish between edge maps from string simulations with and without junctions, we examine the density distribution of edges and pixels crossed by edges. We find that in string simulations without Gaussian noise (such as produced by the dominant inflationary fluctuations) our analysis of the output data from the Canny algorithm can clearly distinguish between simulations with and without string junctions. In the presence of Gaussian noise at the level expected from the current bounds on the contribution of cosmic strings to the total power spectrum of density fluctuations, the distinction between models with and without junctions is more difficult. However, by carefully analyzing the data the models can still be differentiated.Comment: 15 page

An Efficient Ant-Based Edge Detector

1st International Conference on Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems, ICCCI 2009 -- 5 October 2009 through 7 October 2009 -- Wroclaw -- 82033An efficient ant-based edge detector is presented. It is based on the distribution of ants on an image, ants try to find possible edges by using a state transition function based on 5x5 edge structures. Visual comparisons show that the proposed method gives finer details and thinner edges at lesser computational times when compared to earlier ant-based approaches. When compared to standard edge detectors, it shows robustness to Gaussian and Salt & Pepper noise and provides finer details than others with same parameter set in both clear and noisy images. © 2010 Springer-Verlag Berlin Heidelberg