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

Suyog Garg; Feng-Li Lin; Kipp Cannon

doi:10.1103/h92m-k44j

paper

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

arXiv:2511.12642 · doi:10.1103/h92m-k44j

Abstract

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 $[5, 75] M_{⊙}$ with a mass ratio limit at $10 M_{⊙}$ , while the spins are uniform in $[- 0.99, 0.99]$ . Our model is able to generate $1 0^{3}$ 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 $\sim 1 0^{- 2}$ , with better performance in a restricted parameter space of $χ_{eff} \in [- 0.80, 0.80]$ . The latent sampling error of our model can be quantified at a median mismatch standard deviation of $4 \times 1 0^{- 3}$ . 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.

14 pages, 10 figures, submitted to PRD

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

Abstract

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