The authors use convolutional neural networks to classify gravitational wave signals as either consistent with general relativity (GR) or deviating from it, as a test of Einstein's theory. They train on 173 real black-hole merger events, generating GR waveforms and creating modified beyond-GR variants by adding controlled phase deformations. A key finding is that feeding the CNN a "response function" observable (derived from waveform mismatch, isolating the effect of phase deviations) improves classification sensitivity 33-fold compared to using raw whitened waveforms — showing the input representation matters as much as the network architecture.
Main takeaways:
- CNNs can classify gravitational waves as GR-consistent vs. modified-gravity, trained on realistic black-hole merger data
- Using a response function (an observable isolating phase deviations from the bulk signal) as CNN input boosts sensitivity ~33× vs. raw waveforms
- The choice of observable representation is as important as the classifier design itself
- Applied to massive gravity theory, the classifier detects deviations for graviton masses around 10^-23 eV/c² with current detector sensitivity