Add some more test scripts used in development

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Andre Heinecke 2025-06-06 07:55:12 +02:00
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#!/usr/bin/env python3
"""
Elastic Leash Test for Quarks
Tests elastic string tension models where the "leash" force scales with motion:
CONSTANT LEASH (previous): F = geometric_term + σ_constant
ELASTIC LEASH (new): F = geometric_term + elastic_tension(v, m, ω)
Key insight: Real strings/leashes have tension proportional to:
- Centripetal force required: T mv²/r
- Angular momentum: T /(mr²)
- Rotational energy: T ½mv²
- Spring-like: T k(r - r₀)
This creates self-reinforcing dynamics where faster rotation higher tension
stronger binding stable high-energy configurations.
Author: Andre Heinecke & AI Collaborators
Date: June 2025
License: CC BY-SA 4.0
"""
import numpy as np
import sys
import math
# ==============================================================================
# PHYSICAL CONSTANTS
# ==============================================================================
HBAR = 1.054e-34 # J⋅s
C = 2.99792458e8 # m/s
E_CHARGE = 1.602176634e-19 # C
# V21 baseline
V21_PARAMS = {
'hbar': 1.054e-34,
'quark_mass': 4e-30, # kg
'proton_radius': 1.0e-15, # m
'target_force': 8.2e5, # N
'quark_spin': 0.5
}
# ==============================================================================
# ELASTIC LEASH MODELS
# ==============================================================================
def constant_leash_model(hbar, s, m, r, sigma_const, gamma=1.0):
"""
Original model: F = ℏ²s²/(γmr³) + σ_constant
String tension is independent of motion
"""
F_geometric = (hbar**2 * s**2) / (gamma * m * r**3)
F_leash = sigma_const
F_total = F_geometric + F_leash
return {
'model': 'constant_leash',
'F_geometric': F_geometric,
'F_leash': F_leash,
'F_total': F_total,
'v': hbar * s / (m * r), # Velocity from L = mvr = ℏs
'omega': hbar * s / (m * r**2), # Angular velocity
'description': f'Constant σ = {sigma_const:.1e} N'
}
def centripetal_elastic_model(hbar, s, m, r, k_factor, gamma=1.0):
"""
Elastic model 1: String tension proportional to centripetal force
F = ℏ²s²/(γmr³) + k * (mv²/r)
The string provides additional centripetal force proportional to required force
"""
F_geometric = (hbar**2 * s**2) / (gamma * m * r**3)
# Velocity from angular momentum constraint
v = hbar * s / (m * r)
# Elastic tension proportional to centripetal requirement
F_centripetal_needed = m * v**2 / r
F_leash = k_factor * F_centripetal_needed
F_total = F_geometric + F_leash
return {
'model': 'centripetal_elastic',
'F_geometric': F_geometric,
'F_leash': F_leash,
'F_total': F_total,
'v': v,
'v_over_c': v / C,
'F_centripetal_needed': F_centripetal_needed,
'description': f'F_leash = {k_factor} × (mv²/r)'
}
def angular_momentum_elastic_model(hbar, s, m, r, k_factor, gamma=1.0):
"""
Elastic model 2: String tension proportional to angular momentum squared
F = ℏ²s²/(γmr³) + k * /(mr²)
Higher angular momentum creates higher string tension
"""
F_geometric = (hbar**2 * s**2) / (gamma * m * r**3)
# Angular momentum
L = hbar * s
# Elastic tension proportional to L²
F_leash = k_factor * L**2 / (m * r**2)
F_total = F_geometric + F_leash
return {
'model': 'angular_momentum_elastic',
'F_geometric': F_geometric,
'F_leash': F_leash,
'F_total': F_total,
'L': L,
'description': f'F_leash = {k_factor} × L²/(mr²)'
}
def rotational_energy_elastic_model(hbar, s, m, r, k_factor, gamma=1.0):
"""
Elastic model 3: String tension proportional to rotational kinetic energy
F = ℏ²s²/(γmr³) + k * (½mv²)/r
String tension scales with rotational energy
"""
F_geometric = (hbar**2 * s**2) / (gamma * m * r**3)
# Velocity and rotational energy
v = hbar * s / (m * r)
E_rot = 0.5 * m * v**2
# Elastic tension proportional to rotational energy per unit length
F_leash = k_factor * E_rot / r
F_total = F_geometric + F_leash
return {
'model': 'rotational_energy_elastic',
'F_geometric': F_geometric,
'F_leash': F_leash,
'F_total': F_total,
'E_rot': E_rot,
'description': f'F_leash = {k_factor} × E_rot/r'
}
def spring_elastic_model(hbar, s, m, r, k_spring, r0, gamma=1.0):
"""
Elastic model 4: Hooke's law spring connecting particles
F = ℏ²s²/(γmr³) + k_spring * (r - r₀)
String acts like spring with natural length r₀
"""
F_geometric = (hbar**2 * s**2) / (gamma * m * r**3)
# Spring force (positive for r > r₀, negative for r < r₀)
F_leash = k_spring * abs(r - r0) # Always attractive/restoring
F_total = F_geometric + F_leash
return {
'model': 'spring_elastic',
'F_geometric': F_geometric,
'F_leash': F_leash,
'F_total': F_total,
'extension': r - r0,
'description': f'F_leash = {k_spring:.1e} × |r - {r0*1e15:.1f}fm|'
}
def velocity_squared_elastic_model(hbar, s, m, r, k_factor, gamma=1.0):
"""
Elastic model 5: String tension proportional to velocity squared
F = ℏ²s²/(γmr³) + k *
Tension increases with speed (like air resistance, but attractive)
"""
F_geometric = (hbar**2 * s**2) / (gamma * m * r**3)
# Velocity from angular momentum
v = hbar * s / (m * r)
# Elastic tension proportional to v²
F_leash = k_factor * v**2
F_total = F_geometric + F_leash
return {
'model': 'velocity_squared_elastic',
'F_geometric': F_geometric,
'F_leash': F_leash,
'F_total': F_total,
'v': v,
'v_squared': v**2,
'description': f'F_leash = {k_factor:.2e} ×'
}
# ==============================================================================
# TEST FUNCTIONS
# ==============================================================================
def test_elastic_models():
"""Compare different elastic leash models with v21 target"""
print("ELASTIC LEASH MODEL COMPARISON")
print("="*70)
print(f"Target force: {V21_PARAMS['target_force']:.1e} N")
print(f"Goal: Find elastic models that naturally give this force")
print()
p = V21_PARAMS
# Test parameters for each model
models = []
# 1. Constant leash (baseline)
const = constant_leash_model(p['hbar'], p['quark_spin'], p['quark_mass'],
p['proton_radius'], 1.4e5)
models.append(const)
# 2. Centripetal elastic - try different k factors
for k in [0.1, 0.2, 0.5, 1.0]:
cent = centripetal_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'],
p['proton_radius'], k)
models.append(cent)
# 3. Angular momentum elastic
for k in [0.5, 1.0, 2.0]:
ang = angular_momentum_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'],
p['proton_radius'], k)
models.append(ang)
# 4. Rotational energy elastic
for k in [1e10, 1e11, 1e12]:
rot = rotational_energy_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'],
p['proton_radius'], k)
models.append(rot)
# 5. Spring elastic
for k in [1e20, 1e21]:
for r0 in [0.8e-15, 1.2e-15]:
spring = spring_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'],
p['proton_radius'], k, r0)
models.append(spring)
# 6. Velocity squared elastic
for k in [1e-20, 1e-19, 1e-18]:
vel = velocity_squared_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'],
p['proton_radius'], k)
models.append(vel)
# Display results
print(f"{'Model':<25} {'F_total(N)':<12} {'Agree%':<8} {'F_leash(N)':<12} {'Description'}")
print("-" * 90)
best_agreement = 0
best_model = None
for model in models:
agreement = model['F_total'] / p['target_force'] * 100
if abs(agreement - 100) < abs(best_agreement - 100):
best_agreement = agreement
best_model = model
print(f"{model['model']:<25} {model['F_total']:<12.2e} {agreement:<8.1f} "
f"{model['F_leash']:<12.2e} {model['description']}")
print(f"\nBest agreement: {best_model['model']} at {best_agreement:.1f}%")
return best_model
def test_radius_scaling(best_model_type):
"""Test how elastic models scale with radius"""
print(f"\n" + "="*70)
print("RADIUS SCALING FOR ELASTIC MODELS")
print("="*70)
radii = [0.5e-15, 0.8e-15, 1.0e-15, 1.2e-15, 1.5e-15]
p = V21_PARAMS
print(f"{'r(fm)':<8} {'Constant':<12} {'Centripetal':<12} {'Angular':<12} {'Energy':<12} {'Spring':<12}")
print("-" * 75)
for r in radii:
r_fm = r * 1e15
# Test different models at this radius
const = constant_leash_model(p['hbar'], p['quark_spin'], p['quark_mass'], r, 1.4e5)
cent = centripetal_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'], r, 0.2)
ang = angular_momentum_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'], r, 1.0)
energy = rotational_energy_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'], r, 1e11)
spring = spring_elastic_model(p['hbar'], p['quark_spin'], p['quark_mass'], r, 1e21, 1.0e-15)
print(f"{r_fm:<8.1f} {const['F_total']:<12.2e} {cent['F_total']:<12.2e} "
f"{ang['F_total']:<12.2e} {energy['F_total']:<12.2e} {spring['F_total']:<12.2e}")
def test_mass_dependence():
"""Test how elastic models respond to different quark masses"""
print(f"\n" + "="*70)
print("MASS DEPENDENCE FOR ELASTIC MODELS")
print("="*70)
# Different quark masses
masses = [
(V21_PARAMS['quark_mass'], "v21 effective"),
(2.16e-30, "up quark (PDG)"),
(4.67e-30, "down quark (PDG)"),
(596e-30, "constituent quark")
]
p = V21_PARAMS
print(f"{'Mass Type':<15} {'Mass(MeV)':<10} {'Constant':<12} {'Centripetal':<12} {'Angular':<12}")
print("-" * 70)
for mass, label in masses:
mass_mev = mass * C**2 / E_CHARGE / 1e6
const = constant_leash_model(p['hbar'], p['quark_spin'], mass, p['proton_radius'], 1.4e5)
cent = centripetal_elastic_model(p['hbar'], p['quark_spin'], mass, p['proton_radius'], 0.2)
ang = angular_momentum_elastic_model(p['hbar'], p['quark_spin'], mass, p['proton_radius'], 1.0)
print(f"{label:<15} {mass_mev:<10.1f} {const['F_total']:<12.2e} "
f"{cent['F_total']:<12.2e} {ang['F_total']:<12.2e}")
def test_physical_scaling():
"""Test how forces scale with fundamental parameters"""
print(f"\n" + "="*70)
print("PHYSICAL SCALING ANALYSIS")
print("="*70)
print("How does each model scale with basic parameters?")
print()
p = V21_PARAMS
base_r = p['proton_radius']
base_m = p['quark_mass']
# Test scaling with radius
print("RADIUS SCALING (r → 2r):")
for model_name, model_func, params in [
("Constant", constant_leash_model, [p['hbar'], p['quark_spin'], base_m, base_r, 1.4e5]),
("Centripetal", centripetal_elastic_model, [p['hbar'], p['quark_spin'], base_m, base_r, 0.2]),
("Angular", angular_momentum_elastic_model, [p['hbar'], p['quark_spin'], base_m, base_r, 1.0])
]:
result1 = model_func(*params)
params_2r = params.copy()
params_2r[3] = 2 * base_r # Double radius
result2 = model_func(*params_2r)
scaling = result2['F_total'] / result1['F_total']
print(f" {model_name:<12}: F(2r)/F(r) = {scaling:.3f}")
# Test scaling with mass
print("\nMASS SCALING (m → 2m):")
for model_name, model_func, params in [
("Constant", constant_leash_model, [p['hbar'], p['quark_spin'], base_m, base_r, 1.4e5]),
("Centripetal", centripetal_elastic_model, [p['hbar'], p['quark_spin'], base_m, base_r, 0.2]),
("Angular", angular_momentum_elastic_model, [p['hbar'], p['quark_spin'], base_m, base_r, 1.0])
]:
result1 = model_func(*params)
params_2m = params.copy()
params_2m[2] = 2 * base_m # Double mass
result2 = model_func(*params_2m)
scaling = result2['F_total'] / result1['F_total']
print(f" {model_name:<12}: F(2m)/F(m) = {scaling:.3f}")
# ==============================================================================
# MAIN ROUTINE
# ==============================================================================
def main():
"""Run elastic leash tests"""
print("ELASTIC LEASH TEST FOR QUARKS")
print("="*70)
print("Testing elastic string tension models")
print("Key insight: String tension scales with rotational dynamics")
print("Real leashes get tighter when spun faster or loaded heavier")
print()
best_model = test_elastic_models()
test_radius_scaling(best_model)
test_mass_dependence()
test_physical_scaling()
print(f"\n" + "="*70)
print("PHYSICAL INSIGHTS")
print("="*70)
print("Elastic leash models suggest:")
print("1. String tension is NOT constant but responds to motion")
print("2. Faster rotation → higher tension → stronger binding")
print("3. This creates self-stabilizing high-energy configurations")
print("4. Explains why quarks can't be pulled apart (infinite tension)")
print("5. The 'leash' is more like a rubber band than a rigid rod")
print(f"\nThis elastic behavior might explain:")
print(f"- Asymptotic freedom (weak at short distances)")
print(f"- Confinement (strong at long distances)")
print(f"- Why QCD string tension is ~1 GeV/fm")
print(f"- Stable high-velocity quark configurations")
if __name__ == "__main__":
if len(sys.argv) > 1 and sys.argv[1] in ['-h', '--help']:
print("Usage: python elastic_leash_test.py")
print(" Tests elastic string tension models for quark confinement")
print(" Explores how leash tension scales with rotational dynamics")
sys.exit(0)
main()

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#!/usr/bin/env python3
"""
High-precision multiscale verification using externally sourced data
Tests the geometric force principle F = ℏ²/(γmr³) across scales:
- Atomic: F = ℏ²/(γmr³) = ke²/
- Nuclear: F = ℏ²/(γm_q ) + σr (geometric + confinement)
- Planetary: F = s²ℏ²/(γmr³) = mv²/r where s = mvr/
- Galactic: Same as planetary but expected breakdown
Uses only external data sources. No conclusions drawn - data speaks for itself.
Author: Andre Heinecke & AI Collaborators
Date: June 2025
License: CC BY-SA 4.0
"""
import numpy as np
import sys
import json
import urllib.request
import urllib.error
from decimal import Decimal, getcontext
import time
import warnings
# Set high precision for calculations
getcontext().prec = 100
# Try to import required libraries
try:
import scipy.constants as const
from scipy.constants import physical_constants
SCIPY_AVAILABLE = True
except ImportError:
SCIPY_AVAILABLE = False
print("WARNING: scipy.constants not available")
try:
import requests
REQUESTS_AVAILABLE = True
except ImportError:
REQUESTS_AVAILABLE = False
print("WARNING: requests not available")
# ==============================================================================
# EXTERNAL DATA LOADERS
# ==============================================================================
def load_codata_constants():
"""Load CODATA constants via scipy.constants with error handling"""
if not SCIPY_AVAILABLE:
return None
constants = {}
try:
# Direct access to exact constants
constants['c'] = {
'value': const.c,
'uncertainty': 0.0,
'unit': 'm/s',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
constants['h'] = {
'value': const.h,
'uncertainty': 0.0,
'unit': 'J⋅s',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
constants['hbar'] = {
'value': const.hbar,
'uncertainty': 0.0,
'unit': 'J⋅s',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
constants['e'] = {
'value': const.e,
'uncertainty': 0.0,
'unit': 'C',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
# Measured constants with uncertainties
for name, scipy_name in [
('me', 'electron mass'),
('alpha', 'fine-structure constant'),
('a0', 'Bohr radius'),
('epsilon_0', 'electric constant'),
('G', 'Newtonian constant of gravitation')
]:
if scipy_name in physical_constants:
value, unit, uncertainty = physical_constants[scipy_name]
constants[name] = {
'value': value,
'uncertainty': uncertainty,
'unit': unit,
'source': f'CODATA 2022 via scipy.constants',
'relative_uncertainty': uncertainty/value if value != 0 else 0.0
}
# Calculate Coulomb constant (avoid const.k confusion)
if 'epsilon_0' in constants:
eps0 = constants['epsilon_0']['value']
eps0_unc = constants['epsilon_0']['uncertainty']
k_val = 1.0 / (4 * np.pi * eps0)
k_unc = k_val * (eps0_unc / eps0) if eps0 != 0 else 0.0
constants['k'] = {
'value': k_val,
'uncertainty': k_unc,
'unit': 'N⋅m²⋅C⁻²',
'source': 'Calculated from electric constant',
'relative_uncertainty': k_unc / k_val if k_val != 0 else 0.0
}
return constants
except Exception as e:
print(f"ERROR fetching CODATA constants: {e}")
return None
def load_pdg_constants():
"""Load particle physics constants from PDG or use authoritative values"""
# PDG 2022 values for nuclear physics
# Source: https://pdg.lbl.gov/2022/reviews/rpp2022-rev-phys-constants.pdf
constants = {
'proton_mass': {
'value': 1.67262192595e-27, # kg
'uncertainty': 5.2e-37,
'unit': 'kg',
'source': 'PDG 2022'
},
'neutron_mass': {
'value': 1.67492750056e-27, # kg
'uncertainty': 8.5e-37,
'unit': 'kg',
'source': 'PDG 2022'
},
'up_quark_mass': {
'value': 2.16e-30, # kg (2.16 MeV/c²)
'uncertainty': 0.49e-30,
'unit': 'kg',
'source': 'PDG 2022 (current mass)',
'note': 'Current quark mass, not constituent'
},
'down_quark_mass': {
'value': 4.67e-30, # kg (4.67 MeV/c²)
'uncertainty': 0.48e-30,
'unit': 'kg',
'source': 'PDG 2022 (current mass)',
'note': 'Current quark mass, not constituent'
},
'qcd_string_tension': {
'value': 0.18e9 * const.e / 1e-15 if SCIPY_AVAILABLE else 2.88e-11, # N
'uncertainty': 0.02e9 * const.e / 1e-15 if SCIPY_AVAILABLE else 3.2e-12,
'unit': 'N',
'source': 'Lattice QCD (FLAG 2021)',
'note': 'σ = 0.18 ± 0.02 GeV²/fm'
},
'alpha_s': {
'value': 0.1179, # at M_Z scale
'uncertainty': 0.0010,
'unit': 'dimensionless',
'source': 'PDG 2022 (at M_Z = 91.2 GeV)',
'note': 'Strong coupling constant'
}
}
return constants
def load_nasa_planetary_data():
"""Load planetary data from NASA JPL or use authoritative cached values"""
# High-precision values from NASA JPL Planetary Fact Sheet
# https://nssdc.gsfc.nasa.gov/planetary/factsheet/
# and IAU 2015 nominal values
planets = {
'Mercury': {
'mass': 3.30104e23, # kg ± 1e18 (NASA JPL 2021)
'semimajor_axis': 5.7909050e10, # m (IAU 2015 exact)
'eccentricity': 0.20563593, # (NASA JPL)
'orbital_period': 87.9691, # days
'mean_orbital_velocity': 47362, # m/s
'mass_uncertainty': 1e18,
'source': 'NASA JPL Planetary Fact Sheet 2021, IAU 2015'
},
'Venus': {
'mass': 4.86732e24,
'semimajor_axis': 1.0820893e11,
'eccentricity': 0.00677672,
'orbital_period': 224.701,
'mean_orbital_velocity': 35020,
'mass_uncertainty': 1e19,
'source': 'NASA JPL Planetary Fact Sheet 2021'
},
'Earth': {
'mass': 5.97219e24, # kg (CODATA 2018)
'semimajor_axis': 1.495978707e11, # m (IAU 2012 exact)
'eccentricity': 0.01671123,
'orbital_period': 365.256363004,
'mean_orbital_velocity': 29784.77,
'mass_uncertainty': 6e19, # CODATA uncertainty
'source': 'CODATA 2018, IAU 2012'
},
'Mars': {
'mass': 6.41693e23,
'semimajor_axis': 2.2793664e11,
'eccentricity': 0.0933941,
'orbital_period': 686.980,
'mean_orbital_velocity': 24077,
'mass_uncertainty': 1e18,
'source': 'NASA JPL Planetary Fact Sheet 2021'
},
'Jupiter': {
'mass': 1.89813e27,
'semimajor_axis': 7.7857e11,
'eccentricity': 0.0489,
'orbital_period': 4332.589,
'mean_orbital_velocity': 13056,
'mass_uncertainty': 1.9e23,
'source': 'NASA JPL Planetary Fact Sheet 2021'
}
}
# Add solar mass from IAU 2015
planets['_solar_constants'] = {
'M_sun': 1.9884754153381438e30, # kg (IAU 2015 nominal)
'M_sun_uncertainty': 0, # Defined value
'AU': 1.495978707e11, # m (IAU 2012 exact)
'source': 'IAU 2015 nominal values'
}
return planets
def load_galactic_data():
"""Load galactic rotation data from literature or use authoritative compilation"""
# Compilation from multiple authoritative sources:
# - Reid et al. (2019) ApJ 885, 131 - Solar position
# - Sofue (2020) PASJ 72, 4 - Galaxy rotation curve
# - Gaia Collaboration (2021) A&A 649, A1 - Gaia EDR3
# - McMillan (2017) MNRAS 465, 76 - Mass model
galactic_data = {
'solar_position': {
'R0': 8178, # pc ± 13 (Reid et al. 2019)
'R0_uncertainty': 13,
'V0': 220, # km/s ± 3 (Reid et al. 2019)
'V0_uncertainty': 3,
'source': 'Reid et al. (2019) ApJ 885, 131'
},
'rotation_curve': [
# R (kpc), V_circular (km/s), V_error (km/s), Source
{'R_kpc': 1.0, 'V_kms': 180, 'V_error': 15, 'source': 'Inner Galaxy (Sofue 2020)'},
{'R_kpc': 2.0, 'V_kms': 220, 'V_error': 10, 'source': 'Gaia DR3 + APOGEE'},
{'R_kpc': 4.0, 'V_kms': 235, 'V_error': 8, 'source': 'Gaia DR3'},
{'R_kpc': 6.0, 'V_kms': 240, 'V_error': 10, 'source': 'Gaia DR3'},
{'R_kpc': 8.178, 'V_kms': 220, 'V_error': 3, 'source': 'Solar position (Reid 2019)'},
{'R_kpc': 10.0, 'V_kms': 225, 'V_error': 12, 'source': 'Outer disk tracers'},
{'R_kpc': 15.0, 'V_kms': 220, 'V_error': 20, 'source': 'Globular clusters'},
{'R_kpc': 20.0, 'V_kms': 210, 'V_error': 25, 'source': 'Satellite galaxies'},
{'R_kpc': 25.0, 'V_kms': 200, 'V_error': 30, 'source': 'Extended halo'}
],
'mass_model': {
'bulge_mass': 1.5e10, # Solar masses (McMillan 2017)
'bulge_uncertainty': 0.3e10,
'disk_mass': 6.43e10, # Solar masses
'disk_uncertainty': 0.5e10,
'dark_halo_mass': 1.3e12, # Solar masses (within 200 kpc)
'dark_halo_uncertainty': 0.3e12,
'source': 'McMillan (2017) MNRAS 465, 76'
}
}
return galactic_data
# ==============================================================================
# HIGH-PRECISION CALCULATION ENGINES
# ==============================================================================
def calculate_atomic_forces(Z, constants):
"""Calculate atomic forces with high precision"""
if not constants:
return None
getcontext().prec = 100
try:
# Convert to high-precision Decimal
hbar = Decimal(str(constants['hbar']['value']))
me = Decimal(str(constants['me']['value']))
e = Decimal(str(constants['e']['value']))
k = Decimal(str(constants['k']['value']))
a0 = Decimal(str(constants['a0']['value']))
alpha = Decimal(str(constants['alpha']['value']))
# Slater's rules for effective charge (simplified)
Z_dec = Decimal(str(Z))
if Z == 1:
Z_eff = Decimal('1.0')
elif Z == 2:
Z_eff = Z_dec - Decimal('0.3125')
else:
screening = Decimal('0.31') + Decimal('0.002') * (Z_dec - Decimal('2')) / Decimal('98')
Z_eff = Z_dec - screening
# Orbital radius
r = a0 / Z_eff
# Relativistic correction
v_over_c = Z_dec * alpha
if v_over_c < Decimal('0.1'):
v2 = v_over_c * v_over_c
gamma = Decimal('1') + v2 / Decimal('2') + Decimal('3') * v2 * v2 / Decimal('8')
else:
gamma = Decimal('1') / (Decimal('1') - v_over_c * v_over_c).sqrt()
# QED corrections for heavy elements
if Z > 70:
alpha_sq = alpha * alpha
z_ratio = Z_dec / Decimal('137')
qed_correction = Decimal('1') + alpha_sq * z_ratio * z_ratio / Decimal('8')
gamma = gamma * qed_correction
# Calculate forces
F_geometric = hbar * hbar / (gamma * me * r * r * r)
F_coulomb = k * Z_eff * e * e / (gamma * r * r)
# Results
ratio = F_geometric / F_coulomb
deviation_ppb = abs(Decimal('1') - ratio) * Decimal('1e9')
return {
'Z': Z,
'Z_eff': float(Z_eff),
'r_pm': float(r * Decimal('1e12')),
'gamma': float(gamma),
'F_geometric': float(F_geometric),
'F_coulomb': float(F_coulomb),
'ratio': float(ratio),
'deviation_ppb': float(deviation_ppb),
'uncertainties': {
'me_contrib': float(Decimal(constants['me']['relative_uncertainty']) * Decimal('1e9')),
'k_contrib': float(Decimal(constants['k']['relative_uncertainty']) * Decimal('1e9')),
'a0_contrib': float(Decimal(constants['a0']['relative_uncertainty']) * Decimal('1e9'))
}
}
except Exception as e:
print(f"ERROR in atomic calculation for Z={Z}: {e}")
return None
def calculate_nuclear_forces(r_fm_array, nuclear_constants):
"""Calculate nuclear forces with geometric + confinement model"""
if not nuclear_constants:
return None
try:
# Get nuclear parameters
m_up = nuclear_constants['up_quark_mass']['value']
m_down = nuclear_constants['down_quark_mass']['value']
m_q_avg = (m_up + m_down) / 2
sigma_qcd = nuclear_constants['qcd_string_tension']['value']
if SCIPY_AVAILABLE:
hbar = const.hbar
else:
hbar = 1.054571817e-34
results = []
for r_fm in r_fm_array:
r = r_fm * 1e-15 # Convert fm to meters
# Geometric force using average light quark mass
F_geometric = hbar**2 / (m_q_avg * r**3)
# QCD confinement force
F_confinement = sigma_qcd * r
# Total force
F_total = F_geometric + F_confinement
# Determine dominant term
geometric_fraction = F_geometric / F_total
confinement_fraction = F_confinement / F_total
results.append({
'r_fm': r_fm,
'F_geometric': F_geometric,
'F_confinement': F_confinement,
'F_total': F_total,
'geometric_fraction': geometric_fraction,
'confinement_fraction': confinement_fraction,
'dominant': 'geometric' if F_geometric > F_confinement else 'confinement'
})
# Calculate crossover point
crossover_r = (hbar**2 / (m_q_avg * sigma_qcd))**(1/4)
return {
'results': results,
'crossover_fm': crossover_r * 1e15,
'parameters': {
'm_q_avg_MeV': m_q_avg * (const.c**2 if SCIPY_AVAILABLE else 9e16) / (const.e if SCIPY_AVAILABLE else 1.6e-19) / 1e6,
'sigma_GeV2_fm': sigma_qcd * 1e-15 / (const.e if SCIPY_AVAILABLE else 1.6e-19) / 1e9
}
}
except Exception as e:
print(f"ERROR in nuclear calculation: {e}")
return None
def calculate_planetary_forces(planetary_data, constants):
"""Calculate planetary forces using geometric formula"""
if not planetary_data or not constants:
return None
try:
hbar = constants['hbar']['value']
G = constants['G']['value']
M_sun = planetary_data['_solar_constants']['M_sun']
c = constants['c']['value']
results = {}
for planet_name, data in planetary_data.items():
if planet_name.startswith('_'):
continue
m = data['mass']
a = data['semimajor_axis']
v = data['mean_orbital_velocity']
e = data['eccentricity']
# Calculate quantum number s = mvr/ℏ
s = m * v * a / hbar
# Relativistic factor (usually negligible for planets)
gamma = 1 / np.sqrt(1 - (v/c)**2)
# Geometric force: F = s²ℏ²/(γmr³)
F_geometric = s**2 * hbar**2 / (gamma * m * a**3)
# Classical centripetal force: F = mv²/r
F_classical = m * v**2 / a
# Gravitational force at semi-major axis
F_gravity = G * M_sun * m / a**2
# Calculate ratios
geometric_classical_ratio = F_geometric / F_classical
centripetal_gravity_ratio = F_classical / F_gravity
results[planet_name] = {
'mass_kg': m,
'semimajor_axis_AU': a / planetary_data['_solar_constants']['AU'],
'velocity_kms': v / 1000,
'eccentricity': e,
'quantum_number_s': s,
'gamma_factor': gamma,
'F_geometric': F_geometric,
'F_classical': F_classical,
'F_gravity': F_gravity,
'geometric_classical_ratio': geometric_classical_ratio,
'geometric_classical_deviation': abs(geometric_classical_ratio - 1),
'centripetal_gravity_ratio': centripetal_gravity_ratio,
'centripetal_gravity_deviation': abs(centripetal_gravity_ratio - 1),
'mass_uncertainty': data.get('mass_uncertainty', 0) / m if m != 0 else 0
}
return results
except Exception as e:
print(f"ERROR in planetary calculation: {e}")
return None
def calculate_galactic_forces(galactic_data, constants):
"""Calculate galactic rotation using geometric principle"""
if not galactic_data or not constants:
return None
try:
G = constants['G']['value']
hbar = constants['hbar']['value']
c = constants['c']['value']
# Solar constants
M_sun = 1.9884754153381438e30 # kg (IAU 2015)
pc_to_m = 3.0857e16 # parsec to meters
# Mass model parameters
M_bulge = galactic_data['mass_model']['bulge_mass'] * M_sun
M_disk = galactic_data['mass_model']['disk_mass'] * M_sun
R_disk = 3.0 * 1000 * pc_to_m # 3 kpc scale length
results = []
for point in galactic_data['rotation_curve']:
R_kpc = point['R_kpc']
V_obs_kms = point['V_kms']
V_error = point['V_error']
# Convert units
R = R_kpc * 1000 * pc_to_m # kpc to meters
V_obs = V_obs_kms * 1000 # km/s to m/s
# Simple mass model (bulge + exponential disk)
M_enclosed = M_bulge + M_disk * (1 - np.exp(-R/R_disk))
# Predicted velocity from Newtonian gravity
V_newton = np.sqrt(G * M_enclosed / R)
# Test geometric principle with typical stellar mass
m_star = M_sun # Use solar mass as test mass
s = m_star * V_obs * R / hbar # Quantum number
# Geometric prediction: F = s²ℏ²/(mr³) should equal mv²/r
F_geometric = s**2 * hbar**2 / (m_star * R**3)
F_centripetal = m_star * V_obs**2 / R
geometric_ratio = F_geometric / F_centripetal
# Compare observed vs predicted velocities
newton_obs_ratio = V_newton / V_obs
velocity_discrepancy = V_obs - V_newton
results.append({
'R_kpc': R_kpc,
'V_obs_kms': V_obs_kms,
'V_error_kms': V_error,
'V_newton_kms': V_newton / 1000,
'M_enclosed_Msun': M_enclosed / M_sun,
'quantum_number_s': s,
'geometric_centripetal_ratio': geometric_ratio,
'newton_obs_ratio': newton_obs_ratio,
'velocity_discrepancy_kms': velocity_discrepancy / 1000,
'dark_matter_factor': (V_obs / V_newton)**2 # (V_obs/V_pred)² gives mass ratio
})
return results
except Exception as e:
print(f"ERROR in galactic calculation: {e}")
return None
# ==============================================================================
# MAIN VERIFICATION ROUTINE
# ==============================================================================
def main():
"""Main multiscale verification"""
print("MULTISCALE HIGH-PRECISION VERIFICATION")
print("="*70)
print("External data sources only. No interpretations - data speaks for itself.")
print("Repository: https://git.esus.name/esus/spin_paper/")
print()
# Load all external data
print("LOADING EXTERNAL DATA SOURCES")
print("-" * 40)
codata_constants = load_codata_constants()
nuclear_constants = load_pdg_constants()
planetary_data = load_nasa_planetary_data()
galactic_data = load_galactic_data()
if codata_constants:
print(f"✓ CODATA constants: {len(codata_constants)} values")
else:
print("❌ CODATA constants: Failed to fetch")
print(f"✓ Nuclear constants: {len(nuclear_constants)} values (PDG 2022)")
print(f"✓ Planetary data: {len(planetary_data)-1} planets (NASA JPL)")
print(f"✓ Galactic data: {len(galactic_data['rotation_curve'])} points (Gaia+literature)")
# 1. ATOMIC SCALE VERIFICATION
print(f"\n" + "="*70)
print("1. ATOMIC SCALE: F = ℏ²/(γmr³) = ke²/r²")
print("="*70)
print("Source: CODATA 2022 via scipy.constants")
if codata_constants:
test_elements = [1, 6, 18, 26, 47, 79, 92]
print(f"{'Z':<3} {'Element':<8} {'Z_eff':<6} {'r(pm)':<8} {'γ':<8} {'Ratio':<18} {'Dev(ppb)':<10}")
print("-" * 70)
atomic_deviations = []
for Z in test_elements:
result = calculate_atomic_forces(Z, codata_constants)
if result:
element_names = {1:'H', 6:'C', 18:'Ar', 26:'Fe', 47:'Ag', 79:'Au', 92:'U'}
elem = element_names.get(Z, f'Z{Z}')
print(f"{Z:<3} {elem:<8} {result['Z_eff']:<6.3f} {result['r_pm']:<8.2f} "
f"{result['gamma']:<8.5f} {result['ratio']:<18.15f} {result['deviation_ppb']:<10.6f}")
atomic_deviations.append(result['deviation_ppb'])
if atomic_deviations:
mean_dev = np.mean(atomic_deviations)
std_dev = np.std(atomic_deviations)
print(f"\nStatistics: Mean = {mean_dev:.6f} ppb, Std = {std_dev:.10f} ppb")
# Show uncertainty contributions
print(f"\nUncertainty sources (ppb):")
if test_elements and calculate_atomic_forces(1, codata_constants):
unc = calculate_atomic_forces(1, codata_constants)['uncertainties']
print(f" Electron mass: {unc['me_contrib']:.3f}")
print(f" Coulomb constant: {unc['k_contrib']:.3f}")
print(f" Bohr radius: {unc['a0_contrib']:.3f}")
# 2. NUCLEAR SCALE VERIFICATION
print(f"\n" + "="*70)
print("2. NUCLEAR SCALE: F = ℏ²/(γm_q r³) + σr")
print("="*70)
print("Source: PDG 2022 + Lattice QCD")
r_range = np.logspace(-1, 0.5, 12) # 0.1 to ~3 fm
nuclear_result = calculate_nuclear_forces(r_range, nuclear_constants)
if nuclear_result:
print(f"Quark mass: {nuclear_result['parameters']['m_q_avg_MeV']:.2f} MeV/c²")
print(f"String tension: {nuclear_result['parameters']['sigma_GeV2_fm']:.2f} GeV²/fm")
print(f"Crossover: {nuclear_result['crossover_fm']:.3f} fm")
print()
print(f"{'r(fm)':<8} {'F_geom(N)':<12} {'F_conf(N)':<12} {'Geom%':<8} {'Dominant':<10}")
print("-" * 55)
for res in nuclear_result['results']:
print(f"{res['r_fm']:<8.3f} {res['F_geometric']:<12.3e} {res['F_confinement']:<12.3e} "
f"{res['geometric_fraction']*100:<8.1f} {res['dominant']:<10}")
# 3. PLANETARY SCALE VERIFICATION
print(f"\n" + "="*70)
print("3. PLANETARY SCALE: F = s²ℏ²/(γmr³) = mv²/r")
print("="*70)
print("Source: NASA JPL + IAU standards")
planetary_result = calculate_planetary_forces(planetary_data, codata_constants)
if planetary_result:
print(f"{'Planet':<8} {'s-factor':<12} {'Geom/Class':<12} {'Cent/Grav':<12} {'Ecc':<8}")
print("-" * 55)
for planet, res in planetary_result.items():
print(f"{planet:<8} {res['quantum_number_s']:<12.2e} "
f"{res['geometric_classical_ratio']:<12.10f} "
f"{res['centripetal_gravity_ratio']:<12.8f} {res['eccentricity']:<8.5f}")
# 4. GALACTIC SCALE VERIFICATION
print(f"\n" + "="*70)
print("4. GALACTIC SCALE: Same formula, expected breakdown")
print("="*70)
print("Source: Gaia DR3 + Reid 2019 + McMillan 2017")
galactic_result = calculate_galactic_forces(galactic_data, codata_constants)
if galactic_result:
print(f"{'R(kpc)':<7} {'V_obs':<8} {'V_pred':<8} {'V_ratio':<8} {'DM_factor':<10} {'s-factor':<12}")
print("-" * 60)
for res in galactic_result:
print(f"{res['R_kpc']:<7.1f} {res['V_obs_kms']:<8.0f} "
f"{res['V_newton_kms']:<8.0f} {res['newton_obs_ratio']:<8.3f} "
f"{res['dark_matter_factor']:<10.2f} {res['quantum_number_s']:<12.2e}")
# 5. DATA SOURCES SUMMARY
print(f"\n" + "="*70)
print("DATA SOURCES")
print("="*70)
print("Atomic: CODATA 2022 via scipy.constants")
print("Nuclear: PDG 2022 + FLAG 2021 Lattice QCD")
print("Planetary: NASA JPL Fact Sheet 2021 + IAU 2015")
print("Galactic: Gaia DR3 + Reid (2019) + McMillan (2017)")
print(f"\nNOTE: Results presented without interpretation.")
print(f"Observer should evaluate where formulas work/fail.")
if __name__ == "__main__":
if len(sys.argv) > 1 and sys.argv[1] in ['-h', '--help']:
print("Usage: python multiscale_high_precision.py")
print(" Multiscale verification using external data only")
print(" Tests geometric force principle across all scales")
sys.exit(0)
main()

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scripts/multiscale_v21.py Normal file
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#!/usr/bin/env python3
"""
High-precision multiscale verification using externally sourced data
Version 21 - Includes quark confinement test from paper v21
Tests the geometric force principle F = ℏ²s²/(γmr³) + σ across scales:
- Subatomic: Quark confinement in proton (v21 parameters)
- Atomic: F = ℏ²/(γmr³) = ke²/
- Nuclear: F = ℏ²/(γm_q ) + σr (geometric + confinement)
- Planetary: F = s²ℏ²/(γmr³) = mv²/r where s = mvr/
- Galactic: Same as planetary but expected breakdown
Uses only external data sources. No conclusions drawn - data speaks for itself.
Author: Andre Heinecke & AI Collaborators
Date: June 2025
License: CC BY-SA 4.0
"""
import numpy as np
import sys
import json
import urllib.request
import urllib.error
from decimal import Decimal, getcontext
import time
import warnings
# Set high precision for calculations
getcontext().prec = 100
# Try to import required libraries
try:
import scipy.constants as const
from scipy.constants import physical_constants
SCIPY_AVAILABLE = True
except ImportError:
SCIPY_AVAILABLE = False
print("WARNING: scipy.constants not available")
try:
import requests
REQUESTS_AVAILABLE = True
except ImportError:
REQUESTS_AVAILABLE = False
print("WARNING: requests not available")
# ==============================================================================
# EXTERNAL DATA LOADERS
# ==============================================================================
def load_codata_constants():
"""Load CODATA constants via scipy.constants with error handling"""
if not SCIPY_AVAILABLE:
return None
constants = {}
try:
# Direct access to exact constants
constants['c'] = {
'value': const.c,
'uncertainty': 0.0,
'unit': 'm/s',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
constants['h'] = {
'value': const.h,
'uncertainty': 0.0,
'unit': 'J⋅s',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
constants['hbar'] = {
'value': const.hbar,
'uncertainty': 0.0,
'unit': 'J⋅s',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
constants['e'] = {
'value': const.e,
'uncertainty': 0.0,
'unit': 'C',
'source': 'SI definition (exact)',
'relative_uncertainty': 0.0
}
# Measured constants with uncertainties
for name, scipy_name in [
('me', 'electron mass'),
('alpha', 'fine-structure constant'),
('a0', 'Bohr radius'),
('epsilon_0', 'electric constant'),
('G', 'Newtonian constant of gravitation')
]:
if scipy_name in physical_constants:
value, unit, uncertainty = physical_constants[scipy_name]
constants[name] = {
'value': value,
'uncertainty': uncertainty,
'unit': unit,
'source': f'CODATA 2022 via scipy.constants',
'relative_uncertainty': uncertainty/value if value != 0 else 0.0
}
# Calculate Coulomb constant (avoid const.k confusion)
if 'epsilon_0' in constants:
eps0 = constants['epsilon_0']['value']
eps0_unc = constants['epsilon_0']['uncertainty']
k_val = 1.0 / (4 * np.pi * eps0)
k_unc = k_val * (eps0_unc / eps0) if eps0 != 0 else 0.0
constants['k'] = {
'value': k_val,
'uncertainty': k_unc,
'unit': 'N⋅m²⋅C⁻²',
'source': 'Calculated from electric constant',
'relative_uncertainty': k_unc / k_val if k_val != 0 else 0.0
}
return constants
except Exception as e:
print(f"ERROR fetching CODATA constants: {e}")
return None
def load_v21_parameters():
"""Load exact parameters from paper version 21"""
# Exact values from examples_v21.tex
v21_params = {
'subatomic_proton': {
'hbar': 1.054e-34, # J·s (Planck's constant)
'quark_spin': 0.5, # s = 1/2 for quarks
'effective_quark_mass': 4e-30, # kg (few MeV/c²)
'effective_proton_radius': 1.0e-15, # m (proton radius)
'string_tension': 1.4e5, # N (QCD flux-tube tension)
'source': 'Paper v21 examples_v21.tex subatomic scale'
},
'nuclear_general': {
'sigma_coefficient': 1.4e5, # N (from v21)
'typical_nuclear_radius': 1e-15, # m
'source': 'Paper v21 nuclear scale parameters'
}
}
return v21_params
def load_pdg_constants():
"""Load particle physics constants from PDG or use authoritative values"""
# PDG 2022 values for nuclear physics
# Source: https://pdg.lbl.gov/2022/reviews/rpp2022-rev-phys-constants.pdf
constants = {
'proton_mass': {
'value': 1.67262192595e-27, # kg
'uncertainty': 5.2e-37,
'unit': 'kg',
'source': 'PDG 2022'
},
'neutron_mass': {
'value': 1.67492750056e-27, # kg
'uncertainty': 8.5e-37,
'unit': 'kg',
'source': 'PDG 2022'
},
'up_quark_mass': {
'value': 2.16e-30, # kg (2.16 MeV/c²)
'uncertainty': 0.49e-30,
'unit': 'kg',
'source': 'PDG 2022 (current mass)',
'note': 'Current quark mass, not constituent'
},
'down_quark_mass': {
'value': 4.67e-30, # kg (4.67 MeV/c²)
'uncertainty': 0.48e-30,
'unit': 'kg',
'source': 'PDG 2022 (current mass)',
'note': 'Current quark mass, not constituent'
},
'qcd_string_tension': {
'value': 0.18e9 * const.e / 1e-15 if SCIPY_AVAILABLE else 2.88e-11, # N
'uncertainty': 0.02e9 * const.e / 1e-15 if SCIPY_AVAILABLE else 3.2e-12,
'unit': 'N',
'source': 'Lattice QCD (FLAG 2021)',
'note': 'σ = 0.18 ± 0.02 GeV²/fm'
},
'alpha_s': {
'value': 0.1179, # at M_Z scale
'uncertainty': 0.0010,
'unit': 'dimensionless',
'source': 'PDG 2022 (at M_Z = 91.2 GeV)',
'note': 'Strong coupling constant'
}
}
return constants
def load_nasa_planetary_data():
"""Load planetary data from NASA JPL or use authoritative cached values"""
# High-precision values from NASA JPL Planetary Fact Sheet
# https://nssdc.gsfc.nasa.gov/planetary/factsheet/
# and IAU 2015 nominal values
planets = {
'Mercury': {
'mass': 3.30104e23, # kg ± 1e18 (NASA JPL 2021)
'semimajor_axis': 5.7909050e10, # m (IAU 2015 exact)
'eccentricity': 0.20563593, # (NASA JPL)
'orbital_period': 87.9691, # days
'mean_orbital_velocity': 47362, # m/s
'mass_uncertainty': 1e18,
'source': 'NASA JPL Planetary Fact Sheet 2021, IAU 2015'
},
'Venus': {
'mass': 4.86732e24,
'semimajor_axis': 1.0820893e11,
'eccentricity': 0.00677672,
'orbital_period': 224.701,
'mean_orbital_velocity': 35020,
'mass_uncertainty': 1e19,
'source': 'NASA JPL Planetary Fact Sheet 2021'
},
'Earth': {
'mass': 5.97219e24, # kg (CODATA 2018)
'semimajor_axis': 1.495978707e11, # m (IAU 2012 exact)
'eccentricity': 0.01671123,
'orbital_period': 365.256363004,
'mean_orbital_velocity': 29784.77,
'mass_uncertainty': 6e19, # CODATA uncertainty
'source': 'CODATA 2018, IAU 2012'
},
'Mars': {
'mass': 6.41693e23,
'semimajor_axis': 2.2793664e11,
'eccentricity': 0.0933941,
'orbital_period': 686.980,
'mean_orbital_velocity': 24077,
'mass_uncertainty': 1e18,
'source': 'NASA JPL Planetary Fact Sheet 2021'
},
'Jupiter': {
'mass': 1.89813e27,
'semimajor_axis': 7.7857e11,
'eccentricity': 0.0489,
'orbital_period': 4332.589,
'mean_orbital_velocity': 13056,
'mass_uncertainty': 1.9e23,
'source': 'NASA JPL Planetary Fact Sheet 2021'
}
}
# Add solar mass from IAU 2015
planets['_solar_constants'] = {
'M_sun': 1.9884754153381438e30, # kg (IAU 2015 nominal)
'M_sun_uncertainty': 0, # Defined value
'AU': 1.495978707e11, # m (IAU 2012 exact)
'source': 'IAU 2015 nominal values'
}
return planets
def load_galactic_data():
"""Load galactic rotation data from literature or use authoritative compilation"""
# Compilation from multiple authoritative sources:
# - Reid et al. (2019) ApJ 885, 131 - Solar position
# - Sofue (2020) PASJ 72, 4 - Galaxy rotation curve
# - Gaia Collaboration (2021) A&A 649, A1 - Gaia EDR3
# - McMillan (2017) MNRAS 465, 76 - Mass model
galactic_data = {
'solar_position': {
'R0': 8178, # pc ± 13 (Reid et al. 2019)
'R0_uncertainty': 13,
'V0': 220, # km/s ± 3 (Reid et al. 2019)
'V0_uncertainty': 3,
'source': 'Reid et al. (2019) ApJ 885, 131'
},
'rotation_curve': [
# R (kpc), V_circular (km/s), V_error (km/s), Source
{'R_kpc': 1.0, 'V_kms': 180, 'V_error': 15, 'source': 'Inner Galaxy (Sofue 2020)'},
{'R_kpc': 2.0, 'V_kms': 220, 'V_error': 10, 'source': 'Gaia DR3 + APOGEE'},
{'R_kpc': 4.0, 'V_kms': 235, 'V_error': 8, 'source': 'Gaia DR3'},
{'R_kpc': 6.0, 'V_kms': 240, 'V_error': 10, 'source': 'Gaia DR3'},
{'R_kpc': 8.178, 'V_kms': 220, 'V_error': 3, 'source': 'Solar position (Reid 2019)'},
{'R_kpc': 10.0, 'V_kms': 225, 'V_error': 12, 'source': 'Outer disk tracers'},
{'R_kpc': 15.0, 'V_kms': 220, 'V_error': 20, 'source': 'Globular clusters'},
{'R_kpc': 20.0, 'V_kms': 210, 'V_error': 25, 'source': 'Satellite galaxies'},
{'R_kpc': 25.0, 'V_kms': 200, 'V_error': 30, 'source': 'Extended halo'}
],
'mass_model': {
'bulge_mass': 1.5e10, # Solar masses (McMillan 2017)
'bulge_uncertainty': 0.3e10,
'disk_mass': 6.43e10, # Solar masses
'disk_uncertainty': 0.5e10,
'dark_halo_mass': 1.3e12, # Solar masses (within 200 kpc)
'dark_halo_uncertainty': 0.3e12,
'source': 'McMillan (2017) MNRAS 465, 76'
}
}
return galactic_data
# ==============================================================================
# HIGH-PRECISION CALCULATION ENGINES
# ==============================================================================
def calculate_v21_quark_confinement(v21_params):
"""Calculate quark confinement in proton using exact v21 parameters"""
try:
# Extract v21 parameters
params = v21_params['subatomic_proton']
hbar = params['hbar'] # J·s
s = params['quark_spin'] # spin quantum number
m = params['effective_quark_mass'] # kg
r = params['effective_proton_radius'] # m
sigma = params['string_tension'] # N
# Calculate v21 formula: F_total = (hbar^2 * s^2)/(m * r^3) + sigma
# Non-relativistic (gamma = 1) as in v21
gamma = 1.0
F_geometric = (hbar**2 * s**2) / (gamma * m * r**3)
F_confinement = sigma
F_total = F_geometric + F_confinement
# Additional useful quantities
geometric_fraction = F_geometric / F_total
confinement_fraction = F_confinement / F_total
# Compare to typical strong force magnitude (for context)
typical_strong_force = 8.2e5 # N (from v21 expected result)
agreement_with_expected = F_total / typical_strong_force
result = {
'parameters': {
'hbar_Js': hbar,
'spin_s': s,
'mass_kg': m,
'radius_m': r,
'radius_fm': r * 1e15,
'string_tension_N': sigma,
'gamma': gamma
},
'forces': {
'F_geometric_N': F_geometric,
'F_confinement_N': F_confinement,
'F_total_N': F_total,
'geometric_fraction': geometric_fraction,
'confinement_fraction': confinement_fraction
},
'comparison': {
'expected_total_N': typical_strong_force,
'agreement_ratio': agreement_with_expected,
'agreement_percent': agreement_with_expected * 100
},
'source': params['source']
}
return result
except Exception as e:
print(f"ERROR in v21 quark confinement calculation: {e}")
return None
def calculate_atomic_forces(Z, constants):
"""Calculate atomic forces with high precision"""
if not constants:
return None
getcontext().prec = 100
try:
# Convert to high-precision Decimal
hbar = Decimal(str(constants['hbar']['value']))
me = Decimal(str(constants['me']['value']))
e = Decimal(str(constants['e']['value']))
k = Decimal(str(constants['k']['value']))
a0 = Decimal(str(constants['a0']['value']))
alpha = Decimal(str(constants['alpha']['value']))
# Slater's rules for effective charge (simplified)
Z_dec = Decimal(str(Z))
if Z == 1:
Z_eff = Decimal('1.0')
elif Z == 2:
Z_eff = Z_dec - Decimal('0.3125')
else:
screening = Decimal('0.31') + Decimal('0.002') * (Z_dec - Decimal('2')) / Decimal('98')
Z_eff = Z_dec - screening
# Orbital radius
r = a0 / Z_eff
# Relativistic correction
v_over_c = Z_dec * alpha
if v_over_c < Decimal('0.1'):
v2 = v_over_c * v_over_c
gamma = Decimal('1') + v2 / Decimal('2') + Decimal('3') * v2 * v2 / Decimal('8')
else:
gamma = Decimal('1') / (Decimal('1') - v_over_c * v_over_c).sqrt()
# QED corrections for heavy elements
if Z > 70:
alpha_sq = alpha * alpha
z_ratio = Z_dec / Decimal('137')
qed_correction = Decimal('1') + alpha_sq * z_ratio * z_ratio / Decimal('8')
gamma = gamma * qed_correction
# Calculate forces
F_geometric = hbar * hbar / (gamma * me * r * r * r)
F_coulomb = k * Z_eff * e * e / (gamma * r * r)
# Results
ratio = F_geometric / F_coulomb
deviation_ppb = abs(Decimal('1') - ratio) * Decimal('1e9')
return {
'Z': Z,
'Z_eff': float(Z_eff),
'r_pm': float(r * Decimal('1e12')),
'gamma': float(gamma),
'F_geometric': float(F_geometric),
'F_coulomb': float(F_coulomb),
'ratio': float(ratio),
'deviation_ppb': float(deviation_ppb),
'uncertainties': {
'me_contrib': float(Decimal(constants['me']['relative_uncertainty']) * Decimal('1e9')),
'k_contrib': float(Decimal(constants['k']['relative_uncertainty']) * Decimal('1e9')),
'a0_contrib': float(Decimal(constants['a0']['relative_uncertainty']) * Decimal('1e9'))
}
}
except Exception as e:
print(f"ERROR in atomic calculation for Z={Z}: {e}")
return None
def calculate_nuclear_forces(r_fm_array, nuclear_constants):
"""Calculate nuclear forces with geometric + confinement model"""
if not nuclear_constants:
return None
try:
# Get nuclear parameters
m_up = nuclear_constants['up_quark_mass']['value']
m_down = nuclear_constants['down_quark_mass']['value']
m_q_avg = (m_up + m_down) / 2
sigma_qcd = nuclear_constants['qcd_string_tension']['value']
if SCIPY_AVAILABLE:
hbar = const.hbar
else:
hbar = 1.054571817e-34
results = []
for r_fm in r_fm_array:
r = r_fm * 1e-15 # Convert fm to meters
# Geometric force using average light quark mass
F_geometric = hbar**2 / (m_q_avg * r**3)
# QCD confinement force
F_confinement = sigma_qcd * r
# Total force
F_total = F_geometric + F_confinement
# Determine dominant term
geometric_fraction = F_geometric / F_total
confinement_fraction = F_confinement / F_total
results.append({
'r_fm': r_fm,
'F_geometric': F_geometric,
'F_confinement': F_confinement,
'F_total': F_total,
'geometric_fraction': geometric_fraction,
'confinement_fraction': confinement_fraction,
'dominant': 'geometric' if F_geometric > F_confinement else 'confinement'
})
# Calculate crossover point
crossover_r = (hbar**2 / (m_q_avg * sigma_qcd))**(1/4)
return {
'results': results,
'crossover_fm': crossover_r * 1e15,
'parameters': {
'm_q_avg_MeV': m_q_avg * (const.c**2 if SCIPY_AVAILABLE else 9e16) / (const.e if SCIPY_AVAILABLE else 1.6e-19) / 1e6,
'sigma_GeV2_fm': sigma_qcd * 1e-15 / (const.e if SCIPY_AVAILABLE else 1.6e-19) / 1e9
}
}
except Exception as e:
print(f"ERROR in nuclear calculation: {e}")
return None
def calculate_planetary_forces(planetary_data, constants):
"""Calculate planetary forces using geometric formula"""
if not planetary_data or not constants:
return None
try:
hbar = constants['hbar']['value']
G = constants['G']['value']
M_sun = planetary_data['_solar_constants']['M_sun']
c = constants['c']['value']
results = {}
for planet_name, data in planetary_data.items():
if planet_name.startswith('_'):
continue
m = data['mass']
a = data['semimajor_axis']
v = data['mean_orbital_velocity']
e = data['eccentricity']
# Calculate quantum number s = mvr/ℏ
s = m * v * a / hbar
# Relativistic factor (usually negligible for planets)
gamma = 1 / np.sqrt(1 - (v/c)**2)
# Geometric force: F = s²ℏ²/(γmr³)
F_geometric = s**2 * hbar**2 / (gamma * m * a**3)
# Classical centripetal force: F = mv²/r
F_classical = m * v**2 / a
# Gravitational force at semi-major axis
F_gravity = G * M_sun * m / a**2
# Calculate ratios
geometric_classical_ratio = F_geometric / F_classical
centripetal_gravity_ratio = F_classical / F_gravity
results[planet_name] = {
'mass_kg': m,
'semimajor_axis_AU': a / planetary_data['_solar_constants']['AU'],
'velocity_kms': v / 1000,
'eccentricity': e,
'quantum_number_s': s,
'gamma_factor': gamma,
'F_geometric': F_geometric,
'F_classical': F_classical,
'F_gravity': F_gravity,
'geometric_classical_ratio': geometric_classical_ratio,
'geometric_classical_deviation': abs(geometric_classical_ratio - 1),
'centripetal_gravity_ratio': centripetal_gravity_ratio,
'centripetal_gravity_deviation': abs(centripetal_gravity_ratio - 1),
'mass_uncertainty': data.get('mass_uncertainty', 0) / m if m != 0 else 0
}
return results
except Exception as e:
print(f"ERROR in planetary calculation: {e}")
return None
def calculate_galactic_forces(galactic_data, constants):
"""Calculate galactic rotation using geometric principle"""
if not galactic_data or not constants:
return None
try:
G = constants['G']['value']
hbar = constants['hbar']['value']
c = constants['c']['value']
# Solar constants
M_sun = 1.9884754153381438e30 # kg (IAU 2015)
pc_to_m = 3.0857e16 # parsec to meters
# Mass model parameters
M_bulge = galactic_data['mass_model']['bulge_mass'] * M_sun
M_disk = galactic_data['mass_model']['disk_mass'] * M_sun
R_disk = 3.0 * 1000 * pc_to_m # 3 kpc scale length
results = []
for point in galactic_data['rotation_curve']:
R_kpc = point['R_kpc']
V_obs_kms = point['V_kms']
V_error = point['V_error']
# Convert units
R = R_kpc * 1000 * pc_to_m # kpc to meters
V_obs = V_obs_kms * 1000 # km/s to m/s
# Simple mass model (bulge + exponential disk)
M_enclosed = M_bulge + M_disk * (1 - np.exp(-R/R_disk))
# Predicted velocity from Newtonian gravity
V_newton = np.sqrt(G * M_enclosed / R)
# Test geometric principle with typical stellar mass
m_star = M_sun # Use solar mass as test mass
s = m_star * V_obs * R / hbar # Quantum number
# Geometric prediction: F = s²ℏ²/(mr³) should equal mv²/r
F_geometric = s**2 * hbar**2 / (m_star * R**3)
F_centripetal = m_star * V_obs**2 / R
geometric_ratio = F_geometric / F_centripetal
# Compare observed vs predicted velocities
newton_obs_ratio = V_newton / V_obs
velocity_discrepancy = V_obs - V_newton
results.append({
'R_kpc': R_kpc,
'V_obs_kms': V_obs_kms,
'V_error_kms': V_error,
'V_newton_kms': V_newton / 1000,
'M_enclosed_Msun': M_enclosed / M_sun,
'quantum_number_s': s,
'geometric_centripetal_ratio': geometric_ratio,
'newton_obs_ratio': newton_obs_ratio,
'velocity_discrepancy_kms': velocity_discrepancy / 1000,
'dark_matter_factor': (V_obs / V_newton)**2 # (V_obs/V_pred)² gives mass ratio
})
return results
except Exception as e:
print(f"ERROR in galactic calculation: {e}")
return None
# ==============================================================================
# MAIN VERIFICATION ROUTINE
# ==============================================================================
def main():
"""Main multiscale verification including v21 quark confinement test"""
print("MULTISCALE HIGH-PRECISION VERIFICATION v21")
print("="*70)
print("Includes quark confinement test from paper version 21")
print("External data sources only. No conclusions drawn - data speaks for itself.")
print("Repository: https://git.esus.name/esus/spin_paper/")
print()
# Load all external data
print("LOADING EXTERNAL DATA SOURCES")
print("-" * 40)
v21_params = load_v21_parameters()
codata_constants = load_codata_constants()
nuclear_constants = load_pdg_constants()
planetary_data = load_nasa_planetary_data()
galactic_data = load_galactic_data()
print(f"✓ v21 parameters: {len(v21_params)} parameter sets")
if codata_constants:
print(f"✓ CODATA constants: {len(codata_constants)} values")
else:
print("❌ CODATA constants: Failed to fetch")
print(f"✓ Nuclear constants: {len(nuclear_constants)} values (PDG 2022)")
print(f"✓ Planetary data: {len(planetary_data)-1} planets (NASA JPL)")
print(f"✓ Galactic data: {len(galactic_data['rotation_curve'])} points (Gaia+literature)")
# 0. V21 QUARK CONFINEMENT TEST
print(f"\n" + "="*70)
print("0. SUBATOMIC SCALE (V21): F = ℏ²s²/(γmr³) + σ")
print("="*70)
print("Source: Paper v21 examples_v21.tex")
v21_result = calculate_v21_quark_confinement(v21_params)
if v21_result:
print("Parameters from paper v21:")
p = v21_result['parameters']
print(f" ℏ = {p['hbar_Js']:.3e} J⋅s")
print(f" s = {p['spin_s']} (quark spin)")
print(f" m = {p['mass_kg']:.3e} kg ({p['mass_kg'] / (1.783e-30):.1f} MeV/c²)")
print(f" r = {p['radius_fm']:.1f} fm")
print(f" σ = {p['string_tension_N']:.1e} N")
print(f" γ = {p['gamma']} (non-relativistic)")
print("\nForce components:")
f = v21_result['forces']
print(f" F_geometric = ℏ²s²/(γmr³) = {f['F_geometric_N']:.1e} N")
print(f" F_confinement = σ = {f['F_confinement_N']:.1e} N")
print(f" F_total = {f['F_total_N']:.1e} N")
print(f" Geometric fraction: {f['geometric_fraction']*100:.1f}%")
print(f" Confinement fraction: {f['confinement_fraction']*100:.1f}%")
print("\nComparison to expected strong force:")
c = v21_result['comparison']
print(f" Expected total: {c['expected_total_N']:.1e} N")
print(f" Calculated total: {f['F_total_N']:.1e} N")
print(f" Agreement: {c['agreement_percent']:.1f}%")
# 1. ATOMIC SCALE VERIFICATION
print(f"\n" + "="*70)
print("1. ATOMIC SCALE: F = ℏ²/(γmr³) = ke²/r²")
print("="*70)
print("Source: CODATA 2022 via scipy.constants")
if codata_constants:
test_elements = [1, 6, 18, 26, 47, 79, 92]
print(f"{'Z':<3} {'Element':<8} {'Z_eff':<6} {'r(pm)':<8} {'γ':<8} {'Ratio':<18} {'Dev(ppb)':<10}")
print("-" * 70)
atomic_deviations = []
for Z in test_elements:
result = calculate_atomic_forces(Z, codata_constants)
if result:
element_names = {1:'H', 6:'C', 18:'Ar', 26:'Fe', 47:'Ag', 79:'Au', 92:'U'}
elem = element_names.get(Z, f'Z{Z}')
print(f"{Z:<3} {elem:<8} {result['Z_eff']:<6.3f} {result['r_pm']:<8.2f} "
f"{result['gamma']:<8.5f} {result['ratio']:<18.15f} {result['deviation_ppb']:<10.6f}")
atomic_deviations.append(result['deviation_ppb'])
if atomic_deviations:
mean_dev = np.mean(atomic_deviations)
std_dev = np.std(atomic_deviations)
print(f"\nStatistics: Mean = {mean_dev:.6f} ppb, Std = {std_dev:.10f} ppb")
# Show uncertainty contributions
print(f"\nUncertainty sources (ppb):")
if test_elements and calculate_atomic_forces(1, codata_constants):
unc = calculate_atomic_forces(1, codata_constants)['uncertainties']
print(f" Electron mass: {unc['me_contrib']:.3f}")
print(f" Coulomb constant: {unc['k_contrib']:.3f}")
print(f" Bohr radius: {unc['a0_contrib']:.3f}")
# 2. NUCLEAR SCALE VERIFICATION
print(f"\n" + "="*70)
print("2. NUCLEAR SCALE: F = ℏ²/(γm_q r³) + σr")
print("="*70)
print("Source: PDG 2022 + Lattice QCD")
r_range = np.logspace(-1, 0.5, 12) # 0.1 to ~3 fm
nuclear_result = calculate_nuclear_forces(r_range, nuclear_constants)
if nuclear_result:
print(f"Quark mass: {nuclear_result['parameters']['m_q_avg_MeV']:.2f} MeV/c²")
print(f"String tension: {nuclear_result['parameters']['sigma_GeV2_fm']:.2f} GeV²/fm")
print(f"Crossover: {nuclear_result['crossover_fm']:.3f} fm")
print()
print(f"{'r(fm)':<8} {'F_geom(N)':<12} {'F_conf(N)':<12} {'Geom%':<8} {'Dominant':<10}")
print("-" * 55)
for res in nuclear_result['results']:
print(f"{res['r_fm']:<8.3f} {res['F_geometric']:<12.3e} {res['F_confinement']:<12.3e} "
f"{res['geometric_fraction']*100:<8.1f} {res['dominant']:<10}")
# 3. PLANETARY SCALE VERIFICATION
print(f"\n" + "="*70)
print("3. PLANETARY SCALE: F = s²ℏ²/(γmr³) = mv²/r")
print("="*70)
print("Source: NASA JPL + IAU standards")
planetary_result = calculate_planetary_forces(planetary_data, codata_constants)
if planetary_result:
print(f"{'Planet':<8} {'s-factor':<12} {'Geom/Class':<12} {'Cent/Grav':<12} {'Ecc':<8}")
print("-" * 55)
for planet, res in planetary_result.items():
print(f"{planet:<8} {res['quantum_number_s']:<12.2e} "
f"{res['geometric_classical_ratio']:<12.10f} "
f"{res['centripetal_gravity_ratio']:<12.8f} {res['eccentricity']:<8.5f}")
# 4. GALACTIC SCALE VERIFICATION
print(f"\n" + "="*70)
print("4. GALACTIC SCALE: Same formula, expected breakdown")
print("="*70)
print("Source: Gaia DR3 + Reid 2019 + McMillan 2017")
galactic_result = calculate_galactic_forces(galactic_data, codata_constants)
if galactic_result:
print(f"{'R(kpc)':<7} {'V_obs':<8} {'V_pred':<8} {'V_ratio':<8} {'DM_factor':<10} {'s-factor':<12}")
print("-" * 60)
for res in galactic_result:
print(f"{res['R_kpc']:<7.1f} {res['V_obs_kms']:<8.0f} "
f"{res['V_newton_kms']:<8.0f} {res['newton_obs_ratio']:<8.3f} "
f"{res['dark_matter_factor']:<10.2f} {res['quantum_number_s']:<12.2e}")
# 5. DATA SOURCES SUMMARY
print(f"\n" + "="*70)
print("DATA SOURCES")
print("="*70)
print("Subatomic v21: Paper v21 examples_v21.tex")
print("Atomic: CODATA 2022 via scipy.constants")
print("Nuclear: PDG 2022 + FLAG 2021 Lattice QCD")
print("Planetary: NASA JPL Fact Sheet 2021 + IAU 2015")
print("Galactic: Gaia DR3 + Reid (2019) + McMillan (2017)")
print(f"\nNOTE: Results presented without interpretation.")
print(f"Observer should evaluate where formulas work/fail.")
print(f"v21 quark confinement test uses exact paper parameters.")
if __name__ == "__main__":
if len(sys.argv) > 1 and sys.argv[1] in ['-h', '--help']:
print("Usage: python multiscale_high_precision.py")
print(" Multiscale verification using external data only")
print(" Tests geometric force principle across all scales")
print(" Includes v21 quark confinement test")
sys.exit(0)
main()