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 multimessenger follow-ups. In this work, we study autoencoder models for gravitational waveform generation by adopting the best-performing architecture of Liao and Lin [1] to approximate fixed duration 1 s long aligned-spin seobnrv4 inspiral-merger-ringdown waveforms. Our parameter space consists of four parameters, [$m_1$, $m_2$, ${\chi }_1(z)$, ${\chi }_2(z)$]. The masses are uniformly sampled in $[5,75]M_{\odot }$ with a mass ratio limit of $m_1/m_2<10$, while the spins are uniform in $[-0.99,0.99]$. Our model is able to generate ${10}^3$ waveforms in about ${10}^{-1}\mathrm{s}$ at an average speed of $50\mathrm{\mu s}$ per waveform on a graphics processing unit (GPU). This batched GPU generation is about 4 orders of magnitude faster than the serialized CPU generation by the base 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 $\sim {10}^{-2}$, with better accuracy in a restricted parameter space of ${\chi }_{\mathrm{eff}}\in [-0.80,0.80]$, however still less than that of other approaches. The latent sampling error of our model can be quantified at a median mismatch standard deviation of $4\times {10}^{-3}$. Although the accuracy of our model does not enable full production use yet, the model could be useful wherever a high volume of approximate theoretical waveforms are required, for instance, for rapid sky localization.

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• General relativity
• Gravitational wave detection
• Gravitational waves
• Astronomical black holes
• Binary stars
• Neutron stars & pulsars
• Numerical simulations in gravitation & astrophysics