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Nick Kern: "Surrogate Modeling Solutions for Cosmological Parameter Estimation of Future HI Radio Intensity Mapping Surveys ."

Abstract: Next-generation low-frequency radio telescopes are aiming to tomographically map out the 3D structure of primordial hydrogen in the universe during the Epoch of Reionization: the era when the first generation of galaxies generated UV radiation that ionized the surrounding diffuse neutral hydrogen. The potential scientific return of such 3D tomographic maps are immense, however, connecting these observations to underlying physical cosmological parameters is complicated by the theoretical challenge of modeling the relevant physics at computational speeds quick enough to enable exploration of the high dimensional and weakly constrained parameter space. One solution is to use machine learning algorithms to learn the behavior of the sophisticated numerical simulation using a simplified surrogate model and then use the surrogate while performing one's parameter constraints. In this work, we use Gaussian Process regressors to create surrogate models for a numerical simulation of the Epoch of Reionization. We then use these to provide a parameter constraint forecast for the second-generation HERA experiment currently being built in the Karoo desert in South Africa, showing that HERA will nominally place strong constraints on astrophysical parameters and may help strengthen constraints on the amplitude of density fluctuations set by the Planck CMB mission.

Bio: Nick Kern is a PhD student in the Astronomy Department at the University of California, Berkeley. He holds an MA in Astronomy from UC Berkeley, and a BS in Physics and Astrophysics from the University of Michigan. He currently works with the radio astronomy lab at Berkeley on the HERA experiment, which is aiming to map out the distribution of neutral hydrogen in the early universe. His recent work involves using surrogate modeling techniques to relate computationally expensive simulations to future HERA observations in order to perform cosmological parameter estimation.

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