Title: A Bayesian Framework for Cosmic String Searches in CMB Maps,
Abstract: There exists various proposals to detect cosmic strings from Cosmic Microwave
Background (CMB) or 21 cm temperature maps. Current proposals do not aim to
find the location of strings on sky maps, all of these approaches can be
thought of as a statistic on a sky map. We propose a Bayesian interpretation of
cosmic string detection and within that framework, we derive a connection
between estimates of cosmic string locations and cosmic string tension $G\mu$.
We use this Bayesian framework to develop a machine learning framework for
detecting strings from sky maps and outline how to implement this framework
with neural networks. The neural network we trained was able to detect and
locate cosmic strings on noiseless CMB temperature map down to a string tension
of $G\mu=5 \times10^{-9}$ and when analyzing a CMB temperature map that does
not contain strings, the neural network gives a 0.95 probability that
$G\mu\leq2.3\times10^{-9}$.