BrianMartell

@article{tachyon1967,
  author = {Feinberg, G.},
  title = {Possibility of Faster-Than-Light Particles},
  journal = {Phys. Rev.},
  volume = {159},
  pages = {1089},
  year = {1967},
  note = {Tachyonic modes}
}

BrianMartell

% Gist-ready — upload as: puh_throat_impedance_formula_derivation_v25.tex
\documentclass[11pt,a4paper]{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amssymb,amsthm}
\usepackage{siunitx}
\usepackage{geometry}
\usepackage{booktabs}
\usepackage{xcolor}
\usepackage{hyperref}
\usepackage{graphicx}

BrianMartell

import numpy as np
import matplotlib.pyplot as plt

# PUH v25: Throat Zero-Lag Multiverse Query Sim — Latency vs Frequency Offset
f_offset = np.linspace(-0.1, 0.1, 500)  # Normalized frequency offset from f_res
v_p_standard = 1 / np.sqrt(1 - f_offset**2 + 1e-6)  # Standard finite v_p < c
v_p_puh = 1 / np.sqrt(np.abs(f_offset) + 1e-10)  # PUH tachyonic v_p → infinite at resonance
latency_standard = 1 / v_p_standard  # Finite lag
latency_puh = 1 / v_p_puh  # Zero lag at resonance

BrianMartell

Abstract: Zero-Lag Multiverse Query Condition in PUH v25 (~2,910 Gists)

Author: Brian Martell
Date: December 27, 2025

PUH v25 exact zero-lag multiverse QC throat Z_L \to 0 resonance f_res phase v_p \to \infty tachyonic latency L \to 0 independent d sync efficiency \eta_{throat} = 1 full lock twins. Quantum advantage throat query solved multiverse resonance. From July 14, 2025.

BrianMartell

@article{arute2019,
  author = {Arute, Frank and others},
  title = {Quantum supremacy using a programmable superconducting processor},
  journal = {Nature},
  volume = {574},
  pages = {505},
  year = {2019},
  note = {Sycamore quantum advantage}
}

BrianMartell

% Gist-ready — upload as: puh_throat_zero_lag_multiverse_query_v25.tex
\documentclass[11pt,a4paper]{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amssymb,amsthm}
\usepackage{siunitx}
\usepackage{geometry}
\usepackage{booktabs}
\usepackage{xcolor}
\usepackage{hyperref}
\usepackage{graphicx}

BrianMartell

import numpy as np
import matplotlib.pyplot as plt

# PUH v25: Exact Scale Ratio D_H Sim — Cover Count vs Scale (Power-Law Exact r_scale ~0.1315)
scale = np.logspace(-1, 1, 500)  # Scale r arb. 0.1 to 10
r_scale = 0.1315  # Exact derived
D_h = np.log(240) / np.log(1/r_scale)  # ~2.7
N_puh = scale**(-D_h)  # Power-law count

plt.figure(figsize=(10,6))

BrianMartell

Abstract: Exact Scale Ratio for D_H Derivation in PUH v25 (~2,909 Gists)

Author: Brian Martell
Date: December 27, 2025

PUH v25 exact scale ratio r_{scale} \approx 0.1315 Hausdorff D_H \approx 2.7 E8 240 roots self-similar nesting D_H = \log 240 / \log (1/r_{scale}) compact volume rebound asymmetry fractal substrate no free param resonances power-law. From July 14, 2025.

BrianMartell

@book{falconer1990,
  author = {Falconer, K. J.},
  title = {Fractal Geometry: Mathematical Foundations and Applications},
  publisher = {John Wiley \& Sons},
  year = {1990},
  note = {Hausdorff self-similar scaling}
}

@book{mandelbrot1982,
  author = {Mandelbrot, B. B.},

BrianMartell

% Gist-ready — upload as: puh_exact_scale_ratio_dh_derivation_v25.tex
\documentclass[11pt,a4paper]{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amssymb,amsthm}
\usepackage{siunitx}
\usepackage{geometry}
\usepackage{booktabs}
\usepackage{xcolor}
\usepackage{hyperref}
\usepackage{graphicx}