Inferring Cosmic String Tension through the Neural Network Prediction of String Locations in CMB Maps

Abstract

In previous work, we constructed a convolutional neural network used to estimate the location of cosmic strings in simulated cosmic microwave background temperature anisotropy maps. We derived a connection between the estimates of cosmic string locations by this neural network and the posterior probability distribution of the cosmic string tension $G\mu$. Here, we significantly improve the calculation of the posterior distribution of the string tension $G\mu$. We also improve our previous plain convolutional neural network by using residual networks. We apply our new neural network and posterior calculation method to maps from the same simulation used in our previous work and quantify the improvement.

10 pages, 7 figures. v2->v3: minor typos corrected and formatting adjusted to more closely match published version. v1->v2: We have expanded the Introduction and the References, and changed the formatting to MNRAS style

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Cited by in corpus (7)

• Cosmological constraints with deep learning from KiDS-450 weak lensing maps
• Deep Learning the Morphology of Dark Matter Substructure
• Cosmic string loop production functions
• Quasinormal modes of dS and AdS Black Holes: Feedforward neural network method
• Information Theoretic Bounds on Cosmic String Detection in CMB Maps with Noise
• 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