QRUM Multi-Modal Quantum Perturbation Detection Architecture: Correlation-Based Analysis of Open Gravitational-Wave Data
Year: 2026
Type: preprint
Abstract: Background: Detecting weak correlated structures in gravitational-wave observations remains a significant challenge in modern astrophysics. While matched filtering is highly effective for identifying compact binary coalescences, complementary statistical methods may reveal additional correlated features within synchronized detector data. Methods: This work introduces the Quantum Resonance Unified Model (QRUM) as a correlation-based analytical framework applied to publicly available data from the Gravitational Wave Open Science Center (GWOSC). The methodology combines signal preprocessing, detector synchronization, cross-correlation analysis, matched filtering, residual analysis, power spectral density estimation, bootstrap validation, and comparison with non-event control datasets. Results: The proposed workflow was successfully applied to multiple confirmed gravitational-wave events. The analysis identified reproducible detector correlations and localized residual structures. Several analyzed events exhibited local spectral maxima within the 145–155 Hz interval, whereas the examined control datasets did not consistently show comparable behavior. These observations are presented as empirical statistical findings requiring further investigation rather than evidence of new physical phenomena. Conclusions: QRUM provides a reproducible statistical framework for analyzing synchronized detector observations using open gravitational-wave data. The framework is fully testable and explicitly falsifiable, generating experimentally verifiable predictions for future gravitational-wave observations and multi-detector experiments. Its long-term scientific significance depends on independent replication and validation using larger observational datasets.
Author: P., Gabor
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7167278224
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