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This research establishes a quantitative detection threshold for machine-learning models identifying deviations from General Relativity in real LIGO gravitational-wave noise. By combining convolutional neural networks with hand-crafted statistics, the study demonstrates that current ML-only approaches can reliably distinguish specific beyond-GR signal modifications only when the deviation strength exceeds approximately 25%. These findings define a critical limit for unsupervised anomaly detection in astrophysical data, highlighting the challenges of extracting weak quantum-gravity signatures without traditional matched-filter extraction.
Deep Learning Detection of Beyond-General-Relativity Deviations in Gravitational-Wave Signals: A Detection-Threshold Study with Real LIGO Noise
arXiv:2609.19416v1 Announce Type: cross Abstract: We study machine-learning detection of controlled beyond-General-Relativity (beyond-GR) deviations in gravitational-wave signals, using both synthetic aLIGO-PSD noise and real LIGO H1 detector strain. Three deviation families are applied to General-Relativistic inspiral-merger-ringdown waveforms: amplitude modulation, phase modulation, and frequency modulation, each parameterized by a dimensionless strength coefficient $\beta$. A hybrid classifier combining a one-dimensional convolutional neural network with ten hand-crafted waveform statistics is trained on GR and modified waveforms and tested on a deviation type excluded from training. The central result is a quantitative detectability curve as a function of $\beta$. Using the real GW150914 strain as a template and real H1 detector noise, we find a detection threshold at $\beta \approx 0.25$, with accuracy rising smoothly from chance at $\beta \leq 0.2$ to perfect classification at $\beta \geq 0.5$. The threshold value is specific to the quadratic-in-time modulation form adopted here and should not be interpreted as a generic constraint on beyond-GR parameters. We nevertheless argue that the negative result at small $\beta$ is informative: it establishes a quantitative limit on machine-learning-only beyond-GR searches in real detector noise, in the absence of matched-filter signal extraction.
Abstract:We study machine-learning detection of controlled beyond-General-Relativity (beyond-GR) deviations in gravitational-wave signals, using both synthetic aLIGO-PSD noise and real LIGO H1 detector strain. Three deviation families are applied to General-Relativistic inspiral-merger-ringdown waveforms: amplitude modulation, phase modulation, and frequency modulation, each parameterized by a dimensionless strength coefficient $\beta$. A hybrid classifier combining a one-dimensional convolutional neural network with ten hand-crafted waveform statistics is trained on GR and modified waveforms and tested on a deviation type excluded from training. The central result is a quantitative detectability curve as a function of $\beta$. Using the real GW150914 strain as a template and real H1 detector noise, we find a detection threshold at $\beta \approx 0.25$, with accuracy rising smoothly from chance at $\beta \leq 0.2$ to perfect classification at $\beta \geq 0.5$. The threshold value is specific to the quadratic-in-time modulation form adopted here and should not be interpreted as a generic constraint on beyond-GR parameters. We nevertheless argue that the negative result at small $\beta$ is informative: it establishes a quantitative limit on machine-learning-only beyond-GR searches in real detector noise, in the absence of matched-filter signal extraction.
General Relativity and Quantum Cosmology (gr-qc); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
arXiv:2609.19416 [gr-qc]
(or arXiv:2609.19416v1 [gr-qc] for this version)
https://doi.org/10.48550/arXiv.2609.19416 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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