Abstract
With the detection of gravitational waves and use of numerical relativity (NR), we are able to study the properties of powerful astrophysical events. However, due to the complexities of solving Einstein’s equations, other techniques such as surrogate modeling have come into fruition. Surrogate models offer comparable accuracy to that of their NR counterpart, while allowing for waveform evaluations in a fraction of the time. However these too fall short when performing parameter estimations; which can have upwards of millions of waveform evaluations, ramping up total run-time. To mitigate this, we employ the use of GPU-accelerated neural networks, which offer a significant speed-improvement. Using neural networks, we have built a 1D model trained from the hybrid surrogate NRHybSur3dq8 where we target the dominant ℓ = m = 2 mode. Building over the mass-ratio range q ∈ [1, 10], we analyze the overall accuracy in waveform generation and compare total run-times. Further work towards building a 3D model and attaining feature-parity with the surrogate is also explored.