HSAG
CONSORTIUM
For Resonant Research
Resonance Capacity Index (RCI)
A Scalar Diagnostic for Oscillatory Suppression in Precision Instruments
Overview
The Resonance Capacity Index (RCI) is a dimensionless scalar diagnostic developed to quantify the degree of oscillatory suppression and phase compression observed in systems operating within strongly structured electromagnetic and magnetic environments. RCI is defined purely in instrumental terms using measurable phase stability and amplitude behavior.
RCI serves as a compression metric, reducing complex, multi-channel oscillatory dynamics into a single statistically tractable index suitable for cross-instrument comparison.
Within the HSAG framework, RCI is interpreted as a proxy for localized sub-geometric phase coherence affecting signal stability. However, RCI is explicitly theory-agnostic and may be applied independently across optics, time-frequency metrology, and electromagnetic instrumentation.
This separation ensures that:
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RCI remains empirically valid regardless of theoretical interpretation.
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Anomalous suppression effects can be identified without committing to a gravitational hypothesis.
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Alternative physical mechanisms remain open as data grows.
RCI is therefore presented not as confirmation of any particular theory, but as a new observational handle on coherence behavior in extreme precision measurement environments.
What RCI Measures
RCI evaluates how a medium responds to oscillatory input. It is sensitive to:
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Suppression efficiency
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Phase absorption capacity
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Resonant energy collapse potential
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Coherence depletion over time or distance
Rather than asking “How loud is the signal?”, RCI asks “How aggressively does this environment shut oscillations down?”
Why It Matters
Many real-world environments suppress oscillations in ways that are not captured by standard power or amplitude metrics. Examples include:
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Magnetically saturated regions
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Turbulent or storm-driven atmospheric layers
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Phase-disordered plasma environments
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Coherence-active resonant media
RCI provides a medium-independent suppression index that can be compared across different systems and datasets.
Applications
Current and emerging applications of RCI include:
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Atmospheric coherence and storm suppression analysis
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Magnetic storm and magnetospheric oscillation studies
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Remote sensing and interferometric noise suppression
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Optical cavities
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Fiber-optic time-transfer links
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Atomic clocks
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High-stability resonators
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Precision phase-noise platforms
Scientific Status
RCI is currently being developed and tested through:
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Synthetic simulations in controlled resonant fields
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Evaluation on atmospheric proxy data
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Ongoing refinement of numerical stability and interpretation
All published work is released through open repositories with DOI anchoring to support independent validation.
Access and Citation
The core RCI paper and associated materials are available via Zenodo:
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Title: The Resonance Capacity Index (RCI): A Scalar Metric for Oscillation Suppression in Strong Magnetic Regions
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Author: Earl Dixon
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DOI: 10.5281/zenodo.17573988
Researchers and collaborators are invited to reference the work and to contact the HSAG Consortium for Resonant Research for further details.
For Researchers & Reviewers
This paper provides the formal conceptual and computational definition of the Resonance Capacity Index (RCI) — a scalar diagnostic for quantifying oscillation suppression in magnetically active solar regions.
The work formally presents:
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A dimensionless scalar formulation combining:
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Band-limited Doppler oscillation power
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Quiet-Sun reference normalization
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Local magnetic field strength
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A tunable characteristic field scale for empirical calibration
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A full observational computation pipeline using SDO/HMI-class data
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Direct comparison with standard MHD and helioseismic diagnostics
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Use cases for active-region evolution, wave absorption, and data-driven modeling
The RCI is designed as a pre-experimental, observationally grounded diagnostic layer that complements full MHD simulation and helioseismic inversion techniques. It enables rapid comparison across active regions and time intervals while remaining computationally lightweight and interpretable.
The authorship intentionally positions this work as a foundational methods reference, suitable for:
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Independent reproduction
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Algorithmic benchmarking
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Machine-learning feature construction
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Comparative solar activity studies
Correspondence and research inquiries may be directed through the HSAG Consortium for Resonant Research.