Auto-encoder model for faster generation of gravitational waveform approximations

The LIGO-Virgo-KAGRA international collaboration have observed more than 250 gravitational wave events using their global network of observatories. Estimation of source parameters for each of these events requires about a million likelihood computations to properly constrain the posteriors. Each such computation entails solving the general relativity equations to obtain a theoretical waveform, which is then matched against the detected signal. This operation is computationally heavy, especially in the case of complex waveforms. The upcoming gravitational wave observatories, with an estimated 10⁴-10⁶ detections per year, make it imperative to have solutions for the evident bottleneck for rapid parameter estimation. Towards this end, we present an auto-encoder model for generation of effective one-body SEOBNRv4 binary black hole waveforms. We train our model with 27, 300 samples. Our parameter space is made of the two binary component masses: m₁, m₂5, 75\, M_ with a hard mass ratio limit of q=m₁/m₂<10. Our model is able to generate 10⁴ samples in O (1) second, with a median polarization mismatch value of order 10^-3. Our work provides the first step towards having a production ready framework for real-time rapid generation of highly-accurate gravitational waveform approximations. This will enable orders-of-magnitude faster online parameter estimation, while basically providing the same scientific potential.