  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.

Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology;High Energy Physics - Phenomenology;High Energy Physics - Theory