Deep Generative Models of Gravitational Waveforms via Conditional Autoencoder

Understand

We construct few deep generative models of gravitational waveforms based on the semi-supervising scheme of conditional autoencoders and their variational extensions.

• Once the training is done, we find that our best waveform model can generate the inspiral-merger waveforms of binary black hole coalescence with more than $97\%$ average overlap matched filtering accuracy for the mass ratio between $1$ and $10$.
• Besides, the generation time of a single waveform takes about one millisecond, which is about $10$ to $100$ times faster than the EOBNR algorithm running on the same computing facility.
• Moreover, these models can also help to explore the space of waveforms.