Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework
We present a machine learning framework for testing general relativity (GR) with gravitational wave signals from binary black hole mergers. Using the source parameters of 173 BBH events from the GWTC catalog as a realistic astrophysical population, we generate simulated GR waveforms and construct beyond GR (BGR) waveforms by applying controlled phase deformations. We introduce a response function formalism that provides a systematic framework for quantifying how any observable responds to modifications of GR. We train convolutional neural networks (CNNs) on two input representations: whitened waveforms and a response function type observable derived from the waveform mismatch, which isola...
| Authors | Lavinia Heisenberg, Shayan Hemmatyar, Hector Villarrubia-Rojo |
|---|---|
| Primary category | gr-qc |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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