Skip to main content

Modular Time Field Theory — core data structures, modular geometry, gauge-Higgs unification, dark sector, and information geometry

Project description

MTFT — Modular Time Field Theory

Python 3.9+ License: MIT

A Python library implementing the core data structures and computational tools for Modular Time Field Theory (MTFT).

Overview

MTFT proposes a modular time field τ(x) = t_R / t_U mapping spacetime to the upper half-plane , unifying dark matter, particle masses, and cosmic structure through:

  • Modular symmetry — SL(2,ℤ) acting on τ
  • Gauge-Higgs unification — Higgs = A_τ holonomy via the Hosotani mechanism
  • τ-vortex dark matter — logarithmic τ-profiles give flat rotation curves with no dark particles
  • Information geometry bridge — Fisher-Rao curvature of the logistic map connects chaos → geometry → dynamics

The Three-Layer Architecture

Counting   →  Logistic map orbits, figurate-number degeneracies
Geometry   →  Fisher-Rao metric, Ricci curvature R_core
Dynamics   →  SM masses, dark matter halos, decay rates, cosmology

Connected by the spectral determinant identity:

det(1 − q^{1/m} P_m) = η(τ)^{−1/m} θ₃(0,τ)^{1/m}

linking Fredholm determinants (chaos/RG) to CFT partition functions.

Installation

pip install -e .              # minimal (numpy only)
pip install -e ".[full]"      # with scipy + matplotlib
pip install -e ".[dev]"       # with pytest + ruff

Quick Start

import mtft

# ── Modular forms ─────────────────────────────────────
tau = 0.1 + 1.5j
eta = mtft.dedekind_eta(tau)
j   = mtft.forms.j_invariant(tau)
print(f"η(τ) = {eta:.6f}")
print(f"j(τ) = {j:.2f}")

# Verify the spectral determinant identity
result = mtft.forms.verify_spectral_identity(tau, m=2)
print(f"Spectral identity relative error: {result['relative_error']:.2e}")

# ── Hosotani mechanism ────────────────────────────────
hp = mtft.HosotaniPotential(fermion_fraction=0.4, kappa_ew=0.05)
theta0 = hp.find_vacuum()
masses = hp.gauge_masses()
print(f"Vacuum θ₀ = {theta0:.4f}")
print(f"m_W = {masses['m_W']:.2f} GeV  (PDG: 80.37)")
print(f"m_Z = {masses['m_Z']:.2f} GeV  (PDG: 91.19)")

# ── Particle spectrum ─────────────────────────────────
sm = mtft.StandardModel()
top = sm.by_name("Top")
print(f"Top quark: m = {top.mass_GeV} GeV, κ = {top.kappa}")

# Full κ-hierarchy
for name, kappa in sm.kappa_hierarchy():
    print(f"  {name:15s}  κ = {kappa:.2e}")

# ── Dark sector ───────────────────────────────────────
import numpy as np
halo = mtft.TauVortexHalo(A=1e20, r0=1e30)
r = np.logspace(31, 35, 100)
v = halo.v_circular(r)
print(f"v_∞ = {halo.v_infinity:.4e} (flat rotation velocity)")

# ── Information geometry ──────────────────────────────
R = mtft.info_geometry.R_core()
print(f"R_core (Feigenbaum) = {R:.4f}")

# ── Cosmology ─────────────────────────────────────────
cosmo = mtft.FriedmannMTFT(Omega_tau=0.25)
hist = cosmo.expansion_history()

Package Structure

mtft/
├── constants.py       # PDG masses, SM parameters, physical constants
├── modular.py         # τ-field, SL(2,ℤ), hyperbolic geometry
├── forms.py           # Dedekind η, Jacobi θ₃, Eisenstein, spectral det
├── hosotani.py        # Effective potential, vacuum finder, EWSB
├── particles.py       # SM particle database with κ-couplings
├── dark_sector.py     # τ-vortex halos, rotation curves, Tully-Fisher
├── info_geometry.py   # Fisher-Rao metric, Ricci curvature, logistic map
└── cosmology.py       # Modified Friedmann, perturbations, G(t) oscillation

Key Equations

Component Equation
Modular time field τ(x) = t_R(x)/t_U(x) ∈ ℍ
Hyperbolic metric ds² = (dx² + dy²)/y², K = −1
W mass m_W = κ_EW |sin θ_H| / (2R_τ)
Fermion mass m_f = κ_f |sin θ₀| / R_τ
τ-vortex density ρ_τ(r) = A²/(2r²)
Flat rotation v²_∞ = 2πGA²
Tully-Fisher v⁴ ∝ M_BH ∝ M_baryonic
R_core bridge m_τ² = c_m |R_core|

Testing

pytest tests/ -v

License

MIT

Citation

If you use this package in research, please cite:

@software{mtft2025,
  title  = {MTFT: Modular Time Field Theory Python Package},
  author = {Roger},
  year   = {2025},
  url    = {https://github.com/roger/mtft}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mtft-0.3.0.tar.gz (49.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mtft-0.3.0-py3-none-any.whl (50.5 kB view details)

Uploaded Python 3

File details

Details for the file mtft-0.3.0.tar.gz.

File metadata

  • Download URL: mtft-0.3.0.tar.gz
  • Upload date:
  • Size: 49.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.4

File hashes

Hashes for mtft-0.3.0.tar.gz
Algorithm Hash digest
SHA256 7bd6b0df1d2929b58e13ee45d6f71c418386a20c041599917be2513e66f6a918
MD5 91852ec21886a5828834b955d8b2b83e
BLAKE2b-256 6ef24664ea5f5d0fa5eb2cbb85bf7cd70ef2ebcad0d32feb1892d6e9a743136c

See more details on using hashes here.

File details

Details for the file mtft-0.3.0-py3-none-any.whl.

File metadata

  • Download URL: mtft-0.3.0-py3-none-any.whl
  • Upload date:
  • Size: 50.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.4

File hashes

Hashes for mtft-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 aaf918e98bd9d543208a83bef7a4f8c3416d7a827d710ac7a9a4854002f4a88b
MD5 eccadfb23612525e0413566cd00d24b0
BLAKE2b-256 da4c3c5e46c0b74b9480ac6c2824077faf9d39f75cfaec7d1cae1af043d6510c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page