Auto-encoder model for faster generation of effective one-body gravitational waveform approximations

Upgrades to current gravitational wave detectors for the next observation run and upcoming third-generation observatories, like the Einstein telescope, are expected to have enormous improvements in detection sensitivities and compact object merger event rates. Estimation of source parameters for a wider parameter space that these detectable signals will lie in, will be a computational challenge. Thus, it is imperative to have methods to speed-up the likelihood calculations with theoretical waveform predictions, which can ultimately make the parameter estimation faster and aid in rapid multi-messenger follow-ups. In this work we study auto-encoder models for gravitational waveform generation by adopting the best-performing architecture of Liao & Lin (2021) to approximate aligned-spin SEOBNRv4 inspiral-merger-ringdown waveforms. Our parameter space consists of four parameters, [m_1, m_2, χ_1(z), χ_2(z)]. The masses are uniformly sampled in [5,75],M_{\odot} with a mass ratio limit at 10,M_{\odot}, while the spins are uniform in [-0.99,0.99]. Our model is able to generate 10^3 waveforms in \sim 0.1 second at an average speed of about 50 microsecond per waveform on a GPU. This is about 4 orders of magnitude faster than the native SEOBNRv4 implementation, and 2--3 orders of magnitude faster than existing non-machine-learning accelerated waveform variants. The median mismatch for the generated waveforms in the test dataset is \sim10^{-2}, with better performance in a restricted parameter space of χ_{\rm eff}\in[-0.80,0.80]. The latent sampling error of our model can be quantified at a median mismatch standard deviation of 4\times10^{-3}. Although the accuracy of our model does not enable full production-use yet, the model could be useful wherever high-volume of approximate theoretical waveforms are required, for instance, for rapid sky localization.