aiqbench analysis
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)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 291 words
Related coverage
Projection sampling preserves capabilities but diminishes gains from alignment fine-tuning
LessWrong · Oct 16, 2026
State of Pandemic Early Warning
LessWrong · Sep 24, 2026
7 Steps to Mastering Time Series Analysis with Python
KDnuggets · Jun 4, 2026
GLM-5.3 Sometimes Considers How Train-Time Actions Shape Future Behavior
LessWrong · Oct 15, 2026