A unified confluent HeunC framework computes gravitational-wave fluxes from generic Kerr orbits with 10^{-11} relative errors and speedups of 3-60x over existing packages for low- and high-order modes.
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A neural-network-accelerated hierarchical Bayesian pipeline is developed and validated on a phenomenological model to constrain EMRI population parameters from LISA data.
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Efficient and Stable Computation of Gravitational-Wave Fluxes from Generic Kerr Orbits via a Unified HeunC Framework
A unified confluent HeunC framework computes gravitational-wave fluxes from generic Kerr orbits with 10^{-11} relative errors and speedups of 3-60x over existing packages for low- and high-order modes.
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Constraints on the extreme mass-ratio inspiral population from LISA data
A neural-network-accelerated hierarchical Bayesian pipeline is developed and validated on a phenomenological model to constrain EMRI population parameters from LISA data.