A Convolutional Neural Network For Cosmic String Detection in CMB Temperature Maps

Abstract

We present in detail the convolutional neural network used in our previous work to detect cosmic strings in cosmic microwave background (CMB) temperature anisotropy maps. By training this neural network on numerically generated CMB temperature maps, with and without cosmic strings, the network can produce prediction maps that locate the position of the cosmic strings and provide a probabilistic estimate of the value of the string tension $G\mu$. Supplying noiseless simulations of CMB maps with arcmin resolution to the network resulted in the accurate determination both of string locations and string tension for sky maps having strings with string tension as low as $G\mu =5\times 10^{-9}$. The code is publicly available online. Though we trained the network with a long straight string toy model, we show the network performs well with realistic Nambu-Goto simulations.

8 pages, 2 figures. v2 to 3: Introduction shortened and formatting adjustments to more closely match published version. v1 to v2: Cleaned up confusing notation and writing. Added more details and examples to clarify our presentation. Announced the public availability of the code online. Changed the formatting of the paper

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• Inferring Cosmic String Tension through the Neural Network Prediction of String Locations in CMB Maps
• Signal of Cosmic Strings in Cross-Correlation of 21-cm Redshift and CMB Polarization Maps
• The Global 21 cm Signal of a Network of Cosmic String Wakes
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