A Bayesian Framework for Cosmic String Searches in CMB Maps

Abstract

There exists various proposals to detect cosmic strings from Cosmic Microwave Background (CMB) or 21 cm temperature maps. Current proposals do not aim to find the location of strings on sky maps, all of these approaches can be thought of as a statistic on a sky map. We propose a Bayesian interpretation of cosmic string detection and within that framework, we derive a connection between estimates of cosmic string locations and cosmic string tension $G\mu$. We use this Bayesian framework to develop a machine learning framework for detecting strings from sky maps and outline how to implement this framework with neural networks. The neural network we trained was able to detect and locate cosmic strings on noiseless CMB temperature map down to a string tension of $G\mu =5\times 10^{-9}$ and when analyzing a CMB temperature map that does not contain strings, the neural network gives a 0.95 probability that $G\mu \le 2.3\times 10^{-9}$.

21pp, 24 figs. agrees w/ published version. v3->v4: added arXiv no. to ref 36. v2->v3: Corrected typos. Expanded explanations of Eq 2.4, δ_{string}, Eq 3.3, 3.4, KL divergence. Added Fig1b. Relabled Tables1,2 to Tables1a,1b. Fig7,8 changed to log-log scale. v1->v2: Corrected scale in fig 1. Added size and resolution to Fig 1-6, a description of our prior on Gμ, references

References in corpus (26)

• Cosmic String Production Towards the End of Brane Inflation
• Cosmic F- and D-strings
• How generic is cosmic string formation in SUSY GUTs
• Planck 2013 results. XXV. Searches for cosmic strings and other topological defects
• The NANOGrav Nine-year Data Set: Limits on the Isotropic Stochastic Gravitational Wave Background
• Power Spectra in Global Defect Theories of Cosmic Structure Formation
• Scaling from gauge and scalar radiation in Abelian Higgs string networks
• Cosmic String constraints from WMAP and the South Pole Telescope
• COBE vs Cosmic Strings: An Analytical Model
• Doppler peaks from active perturbations
• Constraints on cosmic strings from the LIGO-Virgo gravitational-wave detectors
• Search for Cosmic Strings in CMB Anisotropies
• A Supersymmetric SO(10) Model with Inflation and Cosmic Strings
• Cosmic string loop shapes
• Detecting Cosmic Strings in the CMB with the Canny Algorithm
• Canny Algorithm, Cosmic Strings and the Cosmic Microwave Background
• Edge Detection, Cosmic Strings and the South Pole Telescope
• Searching for Cosmic Strings in CMB Anisotropy Maps using Wavelets and Curvelets
• Wavelet-Bayesian inference of cosmic strings embedded in the cosmic microwave background
• A Convolutional Neural Network For Cosmic String Detection in CMB Temperature Maps
• Optimal Belief Approximation
• The Wouthuysen Field Absorption Trough in Cosmic Strings Wakes
• Disruption of Cosmic String Wakes by Gaussian Fluctuations
• Probing Cosmic Strings with Satellite CMB measurements
• Correlations between 21 cm Radiation and the CMB from Active Sources
• Probing Particle Physics from Top Down with Cosmic Strings

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