Speaker
Description
The major challenge for the fast generation of such waveforms is the generation of the Teukolsky amplitudes for generic (eccentric and inclined) Kerr orbits. The requirement forthe modeling of ∼ 10^5 harmonic modes across a four-dimensional parameter space makes traditional approaches, including direct computation or dense interpolation, computationally prohibitive. To overcome this issue, we introduce a convolutional encoder-decoder architecture for a fast and end-to-end global fitting of the Teukolsky amplitudes. We also adopt a transfer learning strategy to reduce the size of the training dataset, and the model is trained gradually from the simplest Schwarzschild circular orbits to generic Kerr orbits step by step. Within this framework, we obtain a surrogate model based on a semi-analytical Post-Newtonian dataset, and the full harmonic amplitudes can be generated within milliseconds, while the median mode-distribution error for generic orbits is approximately ∼ 10^{−3} . This result indicates that the framework is viable for constructing efficient waveform models for EMRIs.