Searching for Signatures of Cosmic Superstrings in the CMB

TL;DR

This paper investigates whether the presence of string junctions in cosmic superstrings can be detected in CMB anisotropy maps using edge detection algorithms, providing a potential signature to distinguish superstrings from gauge theoretic strings.

Contribution

The study demonstrates that edge detection algorithms can differentiate between cosmic string models with and without junctions in simulated CMB maps, even with realistic noise levels.

Findings

Edge detection can identify string junctions in noiseless simulations.

Distinguishing junctions is more challenging with Gaussian noise, but still feasible.

The method offers a potential observational signature for cosmic superstrings.

Abstract

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.