Internal Decoherence in a Closed-System Cosmology Induces an Effective Stress-Energy Component and Governs Early-Universe Dynamics. Modeling

Overview

We present a technical validation for complete, testable cosmological framework in which the Universe begins in a globally coherent quantum state and the first local act of decoherence inside the closed system creates its own environment. This repository contains a Python-based research module designed to explore quantum entanglement and decoherence as drivers for the expansion and materialization of field modes in closed quantum systems. The modeling focuses on a theoretical cosmological model where the stress-energy tensor emerges dynamically from decoherence processes.

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Requirements

To ensure computational accuracy and reproducibility, the following dependencies must be installed:

• Python: >= 3.8
• NumPy: >= 2.3.3
• Matplotlib: >= 3.10.7

Installation

Clone this repository and set up a Python virtual environment as follows:

# Clone the repository
git clone https://github.com/worktif/matter_framework.git
cd matter_framework

# Create and activate a virtual environment
python3 -m venv env
source env/bin/activate

# Install dependencies
pip install .

Usage

To run the modeling, use the provided analysis.py script in src folder. Below is an example of initializing a simulation and producing results:

from src.modeling.physics_model import CosmologyParams, compute_background_and_spectra, k_phys_to_code
from src.utils.file_system_utils import save_data
from src.utils.plot_utils import plot_charts

# Define cosmological parameters
P = CosmologyParams(
    Omega_r0=9.2e-5,
    Omega_m0=0.315,
    Omega_L0=0.684,

    Omega_ent0=0.05,  # ~5% of critical density today
    epsilon=0.05,  # EoS deformation
    DeltaN=4.0,  # plateau length by N
    N0=-4.0,  # act a few e-folds to "0"

    c_s_scalar=1.0,
    n0=0.4,
    k0=k_phys_to_code(0.05),
    sigma_ln_k=0.4,
    Gamma_over_H=3.0,
    A_ring=0.02,
    phi_ring=0.0
)

# Finalize internal parameter scaling
P.finalize()

# Run the simulation
result = compute_background_and_spectra(
    P,
    N_min=-10.0, N_max=6.0, nN=4001,
    kmin=5e-4, kmax=1.0, nk=256
)

# Plot and save simulation results
plot_charts(result)

# Save analysis data to files
save_data(result)