Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework

TL;DR

This paper introduces a CNN-based framework utilizing response functions to test general relativity with gravitational wave data, significantly improving detection sensitivity for deviations.

Contribution

The work develops a novel response function formalism and demonstrates that CNNs trained on these observables outperform traditional waveform analysis in detecting GR deviations.

Findings

Response function input improves CNN sensitivity by a factor of ~33.

CNN outperforms single feature classifiers at all deformation scales.

Framework detects graviton mass deviations around 10^{-23} eV/c^2.

Abstract

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 isolates the effect of phase deviations from the bulk signal. Using response functions as the CNN input improves the classification sensitivity by a factor of approximately 33 compared to whitened waveforms, demonstrating that the choice of observable representation is as important as the classifier architecture. We study the fundamental limits of this classification through Bayes optimal error analysis, averaging methods that reveal coherent patterns hidden in noise, and a comparison between CNN accuracy and a single feature classifier as a proxy for human performance. At all deformation scales, the CNN outperforms the best single feature approach. We extend the framework to physically motivated theories using the parameterized post Einsteinian (ppE) formalism and apply it to massive gravity, where the classifier detects deviations for graviton masses of order $m_g \sim 10^{-23}\;\mathrm{eV}/c^2$ with aLIGO design sensitivity.