A Bayesian framework for cosmic string searches in CMB maps

Abstract

There exists various proposals to detect cosmic strings from Cosmic Microwave\nBackground (CMB) or 21 cm temperature maps. Current proposals do not aim to\nfind the location of strings on sky maps, all of these approaches can be\nthought of as a statistic on a sky map. We propose a Bayesian interpretation of\ncosmic string detection and within that framework, we derive a connection\nbetween estimates of cosmic string locations and cosmic string tension $G\\mu$.\nWe use this Bayesian framework to develop a machine learning framework for\ndetecting strings from sky maps and outline how to implement this framework\nwith neural networks. The neural network we trained was able to detect and\nlocate cosmic strings on noiseless CMB temperature map down to a string tension\nof $G\\mu=5 \\times10^{-9}$ and when analyzing a CMB temperature map that does\nnot contain strings, the neural network gives a 0.95 probability that\n$G\\mu\\leq2.3\\times10^{-9}$.\n