Generating gravitational waveform libraries of exotic compact binaries with deep learning

TL;DR

This paper demonstrates that deep learning, specifically GANs, can generate accurate gravitational waveforms of exotic compact binaries, reducing reliance on computationally expensive simulations and aiding in gravitational wave data analysis.

Contribution

The study introduces a GAN-based approach to generate gravitational waveforms of exotic compact objects, showing promising accuracy and potential for data augmentation in gravitational wave research.

Findings

GAN can produce 12-25% of waveforms with >95% match to real data.

Neural network predicts waveform match scores with 90% accuracy.

Method reduces need for extensive numerical relativity simulations.

Abstract

Current gravitational wave (GW) detections rely on the existence of libraries of theoretical waveforms. Consequently, finding new physics with GWs requires libraries of non-standard models, which are computationally demanding. We discuss how deep learning frameworks can be used to generate new waveforms "learned" from a simulation dataset obtained, say, from numerical relativity simulations. Concretely, we use the WaveGAN architecture of a generative adversarial network (GAN). As a proof of concept we provide this neural network (NN) with a sample of ($>500$) waveforms from the collisions of exotic compact objects (Proca stars), obtained from numerical relativity simulations. Dividing the sample into a training and a validation set, we show that after a sufficiently large number of training epochs the NN can produce from 12\% to 25\% of the synthetic waveforms with an overlapping match of at least 95\% with the ones from the validation set. We also demonstrate that a NN can be used to predict the overlapping match score, with 90\% of accuracy, of new synthetic samples. These are encouraging results for using GANs for data augmentation and interpolation in the context of GWs, to cover the full parameter space of, say, exotic compact binaries, without the need of intensive numerical relativity simulations.