Pattern Field Theory Analysis of CMB Data Without Healpy

Validating Recursive Field Symmetries in the Pi-Field Substrate

CMB Analysis Result - Pattern Field Theory

This dataset was extracted from the official Planck 2015 full-sky Cosmic Microwave Background (CMB) data using the Commander component separation method at NSIDE 1024 resolution. The original file is available from NASA/IPAC Infrared Science Archive at:

COM_CMB_IQU-commander_1024_R2.02_full.fits – Planck 2015 CMB Data

The converted CSV (Comma-Separated Values) file contains over 12 million rows, each with the following columns:

  • I_STOKES – Intensity (CMB temperature, in Kelvin)
  • Q_STOKES and U_STOKES – Linear polarization parameters (Stokes components)

This simplified CSV format enables cross-platform analysis of recursive asymmetries, Pi–Phi interactions, and two-dimensional to three-dimensional coherence transitions — all predicted by Pattern Field Theory (PFT).

Core Validation Objectives

  • Model convergence of recursive energy fields (Estack series)
  • Detect fractal scale invariance in temperature/polarization clustering
  • Compare anisotropy distributions to Planck drift maps (~1 μK variation)
  • Visualize time-field emergence and entropy vector breaks (as predicted in Axiom 027)

Why This Matters

Conventional cosmology assumes uniformity and isotropy in the early universe. In contrast, Pattern Field Theory predicts directional recursion, localized asymmetry, and emergent coherence patterns — all visible through careful reinterpretation of CMB data.

This experiment was conducted entirely without healpy (the usual HEALPix Python library). Instead, it used only basic Python tools such as astropy (for FITS parsing), scipy, and csv — proving that a full-spectrum CMB field analysis can be performed on ordinary hardware without dependency on legacy map projection tools.

Tools & Formats

  • Input: FITS (Flexible Image Transport System) binary table
  • Output: CSV (~500MB) containing raw I, Q, U Stokes values
  • Software: Python with astropy, scipy, and matplotlib
  • Images: PNG visualizations of field analysis results

Additional derived data such as pixel masks, time-domain series, and alternative coordinate system projections are under development. Full publication and replication instructions will be posted via the official Pattern Field Theory website.

Image Credit: ESA / Planck Collaboration / IPAC Archive