# A Bayesian Framework for Cosmic String Searches in CMB Maps

## TL;DR

This paper introduces a Bayesian framework for detecting and locating cosmic strings in CMB maps, utilizing neural networks to improve sensitivity and provide probabilistic estimates of string tension.

## Contribution

It develops a novel Bayesian interpretation linking string location estimates to string tension and implements a neural network-based detection method.

## Key findings

- Neural network detects cosmic strings with tension down to Gμ=5×10⁻⁹ in noiseless maps.
- The method estimates the probability that Gμ is below a certain threshold.
- Framework provides probabilistic localization of cosmic strings.

## 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 $G\mu\leq2.3\times10^{-9}$.