Planck Limits on Cosmic String Tension Using Machine Learning
Abstract
We develop two parallel machinelearning 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 treebased 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 nontrivial 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 CMBS4like surveys and conservatively find their minimum detectable tension to be $G\mu_{\rm min}\sim 1.9\times 10^{7}$.
 Publication:

arXiv eprints
 Pub Date:
 May 2021
 arXiv:
 arXiv:2106.00059
 Bibcode:
 2021arXiv210600059T
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 11 pages, 7 figures