Cosmological constraints on cosmic strings

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With data about the universe's structure accumulating at an ever-increasing rate, cosmology can now be used to constrain fundamental physics. I am interested in using galaxy surveys, the cosmic microwave background (CMB), and 21 cm cosmology to search for and constrain new physics beyond the particle physics Standard Model (SM), in particular cosmic strings.******Cosmic strings are linear topological defects that can form in a large class of extensions of the SM. Many inflationary scenarios constructed in the context of supergravity models lead to the formation of gauge theory cosmic strings at the end of the inflationary phase, and in a large class of brane inflation models, inflation ends with the formation of a network of cosmic superstrings which can be stabilized as macroscopic objects. Whereas cosmic strings cannot be the dominant source of the primordial fluctuations they can still be a secondary source. Thus searching for cosmic strings probes particle physics beyond the SM in an energy range complementary to that probed by particle accelerators. ******The gravitational effects of the string can be parametrized by its string tension a dimensionless constant which is predicted to be between 1e-8 and 1e-6 for Grand Unified models, and between 1e-12 and 1e-6 for cosmic superstrings. The best robust experimental limit comes from the Planck collaboration's measurement of the CMB angular power spectrum leading to a string tension less than about 1e-7. ******In recent years much research has been done to find a more sensitive probe of cosmic strings in CMB and 21 cm intensity maps. As long cosmic strings move, they accrete matter into over-dense wakes which perturb the CMB light and the 21 cm line in particular. Edge and shape detection algorithms such as the Canny algorithm, as well as wavelets, and curvelets have been proposed as alternatives to the power spectrum in looking for cosmic strings in these maps. While promising, studies of these proposals on numerically simulated maps have not yet yielded significantly stronger limits than those from Planck. Furthermore, all these proposals involve choices and it remains unclear whether or not different choices would improve detection. Finally, none of the above proposals find the location of strings on sky maps. We recently proposed a Bayesian interpretation of cosmic string detection that improves on these shortcomings. Here we present our program to develop machine learning techniques and neural networks to search for cosmic strings in galaxy surveys, CMB, and 21 cm intensity maps. The neural networks will be developed with numerical simulation involving realistic noise, before being used on actual data. We hope to either find evidence for strings or determine that the string tension is below 1e-12 and thus constrain extensions of the SM. Our program will have a significant impact in the training of the next generation of scientists.