Speaker
Description
Numerical relativity (NR) simulations are considered to provide the most faithful representation of the gravitational wave (GW) radiation emitted by binary black hole (BBH) systems. However, in the context of GW astronomy, tasks such as parameter estimation (PE) can require thousands upon thousands of waveform evaluations per second across the entire parameter space. Since performing full NR simulations for each evaluation is not computationally feasible, interpolating methods for existing NR waveforms, known as surrogate models, have been developed with marked success. In this paper, we build on previous work to introduce methods to train a fast surrogate model based on neural networks in order to generate BBH merger waveforms, including the fundamental (2,2) mode, as well as the (3,3), (2,1), (4,4), (3,2), (4,3) and (5,5) higher order modes (HM). Applying a pretraining step on approximant data before fine-tuning on NR data allows us to smooth out the parameter space, and making use of the parallelization ability of GPUs to project the NR waveforms in the $(\theta, \phi)$ sphere during training allows the fitting of all the explored modes simultaneously. The developed surrogate model achieves average mismatches of the order of $10^{-4}$, with the worst mismatch at $2.5\times10^{-3}$, and is able to generate a million waveforms in under 100~ms. Parameter estimation tests show that the addition of higher modes allows for better posteriors when compared to the dominant-mode-only model.