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 Gμ. Here, we significantly improve the calculation of the posterior distribution of the string tension Gμ. 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.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 March 2019
 DOI:
 10.1093/mnras/sty3478
 arXiv:
 arXiv:1810.11889
 Bibcode:
 2019MNRAS.483.5179C
 Keywords:

 methods: data analysis;
 methods: statistical;
 techniques: image processing;
 cosmic background radiation;
 cosmology: theory;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 General Relativity and Quantum Cosmology;
 High Energy Physics  Phenomenology;
 High Energy Physics  Theory
 EPrint:
 10 pages, 7 figures. v2>