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

arXiv: https://arxiv.org/abs/2511.12642 · HTML: https://mlsanity.com/paper/2511.12642/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Position, Padding and Predictions: A Deeper Look at Position Information in CNNs](https://mlsanity.com/paper/2101.12322/) (2021)

## Built on by (most cited first, 1 of 1)

- [Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder](https://mlsanity.com/paper/2609.14277/) (2026)

## Abstract (arXiv)

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, chi1(z), chi2(z)]. The masses are uniformly sampled in [5,75],Modot with a mass ratio limit at 10,Modot, 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 chirm 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.