2025/11/16 by Suyog Garg, Garg, Suyog, Feng-Li Lin +3
Physics and Astronomy · #Pulsars and Gravitational Waves Research #Gamma-ray bursts and supernovae #Quantum Chromodynamics and Particle Interactions
paper · pdf · doi:10.48550/arxiv.2511.12642
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, [m1, m2, χ1(z), χ2(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 103 waveforms in ∼ 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 ∼10-2, with better performance in a restricted parameter space of χ\rm eff∈[-0.80,0.80]. The latent sampling error of our model can be quantified at a median mismatch standard deviation of 4×10-3. 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.