Planck limits on cosmic string tension using machine learning

Abstract

Abstract We develop two parallel machine-learning pipelines to estimate the contribution of cosmic strings (CSs), conveniently encoded in their tension (Gμ), to the anisotropies of the cosmic microwave background radiation observed by Planck. The first approach is tree-based and feeds on certain map features derived by image processing and statistical tools. The second uses convolutional neural network with the goal to explore possible non-trivial features of the CS imprints. The two pipelines are trained on Planck simulations and when applied to Planck SMICA map yield the 3σ upper bound of Gμ ≲ 8.6 × 10−7. We also train and apply the pipelines to make forecasts for futuristic CMB-S4-like surveys and conservatively find their minimum detectable tension to be Gμmin ∼ 1.9 × 10−7.