GitHub - JasonResearch/Brown-J-Invariant: Observer-Normalized Scale Coherence

Brown-J-Invariant

Observer-Normalized Scale Coherence

Observer-Normalized Scale Coherence

Cross-Regime Tests of Acceleration-Domain Inference Using SPARC

This repository contains analysis code and supporting materials for the Observer-Normalized Scale Coherence framework and its empirical tests using disk galaxy data from the SPARC catalog.

The central claim investigated here is that a significant portion of the apparent dynamical discrepancy in galaxies arises from observer-centric normalization mismatch, rather than missing mass or modified dynamics. When inference is renormalized coherently across intrinsically non-equivalent systems, acceleration-domain relations emerge with substantially reduced scatter.

This repository implements that logic across two regimes using a single, frozen inference rule.

Frozen’ here refers to the definitions of observables and the inference mapping, not to dataset-specific quality cuts or regime-appropriate scale proxies.

Scientific Scope

Paper I — Disk Galaxy Coherence (SPARC)

Observer-Normalized Scale Relativity:
Coherence Constraints on Global Physical Inference and Disk Galaxy Dynamics

Paper I introduces a dimensionless acceleration-domain coherence quantity,

[ J \equiv \frac{G M_b}{V^2 R} ]

and demonstrates that when disk galaxies are grouped by intrinsic morphology, this form exhibits substantially reduced scatter relative to density-based normalizations.

The analysis emphasizes:

• Operational independence of observables
• Stability under distance covariance
• Robustness to radius definition
• Residual structure traced to surface brightness (not tuning)

Archived and Related Zenodo Records

This repository is part of a series of archived research artifacts exploring observer-normalized coherence constraints in galactic dynamics and their observational consequences.

• Observer-Normalized Scale Relativity: A Coherence Constraint on Global Physical Inference (core framework):
  https://doi.org/10.5281/zenodo.18356675

• Stress testing across the SPARC galaxy database:
  https://doi.org/10.5281/zenodo.18294837

• Predictive results in the dwarf-galaxy regime:
  https://doi.org/10.5281/zenodo.18308519

• Inference lens demonstration (illustrative code example):
  https://doi.org/10.5281/zenodo.18436273

Paper II — Pre-Registered Dwarf Galaxy Prediction

Observer-Normalized Scale Coherence II:
Execution of a Pre-Registered Prediction in the Dwarf Galaxy Regime

Paper II executes a pre-registered observational prediction defined in Paper I:

Apply the same frozen acceleration-domain coherence logic, unchanged, to the low-mass / dwarf galaxy regime.
If the framework lacks physical content, the prediction should fail.

No new parameters, constants, or regime-specific modifications are introduced.

The same baryonic mass definition, kinematic proxy, and spatial scale are used. The test is designed to be maximally hostile to overfitting.

Repository Contents

Analysis Scripts

• sparc_j_tests.py
  Implements the Paper I analysis:

  • Computes the Newtonian (Q=1) acceleration-domain coherence form
  • Evaluates scatter across morphology bins
  • Stress-tests against distance covariance, radius choice, and surface brightness

• sparc_dwarf_prediction.py
  Implements the Paper II pre-registered prediction:

  • Applies the same coherence logic to low-mass / dwarf systems
  • Computes the predicted dynamical-to-baryonic mass discrepancy: [ \frac{M_{\mathrm{dyn}}}{M_b} = \frac{1}{J} ]
  • Reports robust scatter and residual structure
  • Performs no fitting, tuning, or renormalization

• sparc_j_tests_with_residuals.py
  Diagnostic residual analysis for Paper I:

  • Visualizes median-centered residual structure in J
  • Tests for trivial dependence on mass, velocity, or scale
  • Performs no fitting or correction

Data

• Raw SPARC.xlsx
  SPARC summary table (v1), included unchanged for convenience.

Outputs

Generated output files are written to:

• outputs/ (Paper I)
• outputs_paper2/ (Paper II)

These include CSV summary tables and residual diagnostics.

Methodological Notes

• All observables (mass, velocity, scale) are inferred through independent channels.
• No parameters are fit in any analysis.
• All definitions used in Paper II are frozen from Paper I.
• Distance, radius, and surface-brightness systematics are explicitly tested.
• The same inference rule is applied across mass regimes without modification.

How to Run

Requirements

• Python 3.9+
• Packages:
  • numpy
  • pandas
  • matplotlib (for plots)

Execution

1. Place the scripts and Raw SPARC.xlsx in the same directory
2. Run either analysis:

   python sparc_j_tests.py
   python sparc_dwarf_prediction.py