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 Gµ. 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 Gµ = 5 × 10 −9 and when analyzing a CMB temperature map that does not contain strings, the neural network gives a 0.95 probability that Gµ ≤ 2.3 × 10 −9 .

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 Gµ. 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 Gµ = 5 × 10 −9 , a result from our previous work. In this work we discuss the numerical details of the code that is publicly available online. Furthermore, we show that though we trained the network with a long straight string toy model, the network performs well with realistic Nambu-Goto simulations.

We use a convolutional neural network (CNN) to study cosmic string detection in cosmic microwave background (CMB) flat sky maps with Nambu-Goto strings. On noiseless maps we can measure string tensions down to order 10 −9 , however when noise is included we are unable to measure string tensions below 10 −7 . Motivated by this impasse, we derive an information theoretic bound on the detection of the cosmic string tension Gµ from CMB maps. In particular we bound the information entropy of the posterior distribution of Gµ in terms of the resolution, noise level and total survey area of the CMB map. We evaluate these bounds for the ACT, SPT-3G, Simons Observatory, Cosmic Origins Explorer, and CMB-S4 experiments. These bounds cannot be saturated by any method.