Abstract
The identification of electromagnetic emission from gravitational-wave sources typically requires multiple follow-up observations due to the limited fields-of-view of follow-up observatories compared to the poorly localized direction of gravitational waves. Gravitational-wave localization regions are typically covered with multiple telescope pointings using a "honeycomb" structure, which is optimal only on an infinite, flat surface. Here we present a machine-learning algorithm which uses genetic algorithms along with Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization to find an optimal configuration of tiles to cover the gravitational-wave sky localization area on a spherical surface.
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