http://arxiv.org/abs/2106.00059
We develop two parallel machine-learning pipelines to estimate the contribution of cosmic strings (CSs), conveniently encoded in their tension ($G\mu$), to the anisotropies of the cosmic microwave background radiation observed by {\it 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 {\it Planck} simulations and when applied to {\it Planck} \texttt{SMICA} map yield the $3\sigma$ upper bound of $G\mu\lesssim 8.6\times 10^{-7}$. 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 $G\mu_{\rm min}\sim 1.9\times 10^{-7}$.
M. Torki, H. Hajizadeh, M. Farhang, et. al.
Wed, 2 Jun 21
25/48
Comments: 11 pages, 7 figures
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