Multi-Scale Pipeline for the Search of String-Induced CMB Anisotropies

Abstract

We propose a multi-scale edge-detection algorithm to search for the Gott-Kaiser-Stebbins imprints of a cosmic string (CS) network on the Cosmic Microwave Background (CMB) anisotropies. Curvelet decomposition and extended Canny algorithm are used to enhance the string detectability. Various statistical tools are then applied to quantify the deviation of CMB maps having a cosmic string contribution with respect to pure Gaussian anisotropies of inflationary origin. These statistical measures include the one-point probability density function, the weighted two-point correlation function (TPCF) of the anisotropies, the unweighted TPCF of the peaks and of the up-crossing map, as well as their cross-correlation. We use this algorithm on a hundred of simulated Nambu-Goto CMB flat sky maps, covering approximately $10\%$ of the sky, and for different string tensions $G\mu$. On noiseless sky maps with an angular resolution of $0.9^{\prime }$, we show that our pipeline detects CSs with $G\mu$ as low as $G\mu \gtrsim 4.3\times 10^{-10}$. At the same resolution, but with a noise level typical to a CMB-S4 phase II experiment, the detection threshold would be to $G\mu \gtrsim 1.2\times 10^{-7}$.

13 pages, 5 figures, 1 table, Comments are welcome

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• Cosmic String Detection with Tree-Based Machine Learning
• The Stability of Asymmetric Cylindrical Thin-Shell Wormholes
• Stochastic gravitational waves from long cosmic strings
• Information Theoretic Bounds on Cosmic String Detection in CMB Maps with Noise
• Ridges in the Dark Energy Survey for cosmic trough identification
• Inferring Cosmic String Tension through the Neural Network Prediction of String Locations in CMB Maps
• Planck Limits on Cosmic String Tension Using Machine Learning
• The Global 21 cm Signal of a Network of Cosmic String Wakes
• Cosmic Strings-induced CMB anisotropies in light of Weighted Morphology
• Edge detection with polynomial frames on the sphere