Published June 4, 2026 | Version V.1.0

CGenome: A High-Dimensional Observational Framework for Statistical Classification of Astrophysical Systems

Authors/Creators

  • 1. Independent researcher

Description

CGenome is a purely phenomenological observational framework that represents astrophysical systems as structured vectors in a 20-dimensional feature space.

 

The framework does not introduce new physical laws, modify gravity, or assume causal mechanisms. It defines a unified observational coordinate system intended solely for the statistical organization and classification of astrophysical systems across scales.

 

Each astrophysical system is represented as a 20-dimensional vector in a unified observational manifold:

 

CG = (CG1, CG2, …, CG20)

 

The objective of CGenome is not physical explanation, but statistical classification, structural embedding, and hierarchical organization of cosmic systems.

 

This work is strictly descriptive and does not claim dynamical laws, modifications of General Relativity, or physical causation. It provides a data-driven taxonomy framework for astrophysical structure analysis.

 

The framework is designed for compatibility with clustering, dimensional reduction, and statistical learning techniques, including PCA, UMAP, and HDBSCAN.

 

CGenome can be interpreted as a high-dimensional observational coordinate system for organizing astrophysical complexity rather than a physical theory.

Files

CGenome: A HD-Obs Framework for Stat. Classification of Astrophysical Systems.pdf

Additional details

References

  • Peebles, P. J. E. (1980). The Large-Scale Structure of the Universe. Princeton University Press.
  • Weinberg, S. (2008). Cosmology. Oxford University Press.
  • Springel, V., Frenk, C. S., & White, S. D. M. (2006). "The large-scale structure of the Universe." Nature, 440, 1137–1144.
  • Jaynes, E. T. (1957). "Information Theory and Statistical Mechanics." Physical Review, 106, 620–630.
  • Soltan, A. (1982). "Masses of quasars." Monthly Notices of the Royal Astronomical Society, 200, 115–122.
  • Kormendy, J., & Ho, L. C. (2013). "Coevolution (or not) of supermassive black holes and host galaxies." Annual Review of Astronomy and Astrophysics, 51, 511–653.
  • Bertschinger, E. (1998). "Simulations of structure formation in the universe." Annual Review of Astronomy and Astrophysics, 36, 599–654.
  • Ellis, G. F. R. (2019). Issues in the Philosophy of Cosmology. Cambridge University Press.
  • Pearson, K. (1901). "On lines and planes of closest fit to systems of points in space." Philosophical Magazine, 2, 559–572.
  • Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). "Density-Based Clustering Based on Hierarchical Density Estimates." ECML PKDD.
  • McInnes, L., Healy, J., & Melville, J. (2018). "UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction." arXiv:1802.03426
  • Feigelson, E. D., & Babu, G. J. (2012). Modern Statistical Methods for Astronomy. Cambridge University Press.
  • Ivezic, Z., Connolly, A., VanderPlas, J., & Gray, A. (2014). Statistics, Data Mining, and Machine Learning in Astronomy. Princeton University Press.
  • White, S. D. M., & Rees, M. J. (1978). "Core condensation in heavy halos." Monthly Notices of the Royal Astronomical Society, 183, 341–358.