A Convolutional Neural Network For Cosmic String Detection in CMB
  Temperature Maps
  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
$G\mu=5\times10^{-9}$. 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.
