SHBT Simulator — Static Holographic Boundary Theory

SHBT Simulator is an executable implementation of the Static Holographic Boundary Theory (SHBT), a first‑principles framework that derives gravitational closure, baryogenesis, and observer histories from a completed modular‑invariant boundary CFT. The theory is fully documented in the accompanying paper main.pdf. This simulator serves as an executable proof of the theory: every claim, equation, and numerical prediction in the paper is audited by the Rust/Python code in this repository.

The Big Picture

SHBT postulates that the universe is described by a finite boundary register whose modular‑invariant pairing fixes:

The canonical branch $(k_\ell, k_q, K) = (26, 8, 312)$ with zero framing defect.
The holographic dark‑energy scale $\Lambda_{\rm holo} \simeq 1.09\times 10^{-52},\text{m}^{-2}$.
A finite bit budget $N \simeq 3.31\times 10^{122}$.
A holographic RG flow from boundary entropy densities to symmetric, trace‑normalised metric slices.
A topological baryogenesis identity yielding $\eta_B \simeq 6.45\times 10^{-10}$.
A Causal Point observer interface that crystallises histories only within a local entropy budget.

The paper (main.pdf) is the mathematical formulation. The simulator is the computational verification.

Quick Start (TL;DR)

Clone, build, and run the full paper audit:

git clone https://github.com/sys1own/shbt-simulator.git
cd shbt-simulator

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
source "$HOME/.cargo/env"

# Install maturin and build the Python bindings
pip install maturin
maturin develop --release

# Run the audit
python shbt_simulate.py --mode audit

Paper ⇄ Simulator Relationship

The paper and simulator are developed in lockstep:

Every equation in the paper is implemented in the Rust code.
Every numerical audit table (Sections 8.1–8.4) can be reproduced by running the simulator.
The paper explicitly references simulator objects, e.g.
shbt_simulator.ShbtSimulator().run_full_audit(),
shbt_simulator.StaticBoundary,
shbt_simulator.HolographicProjection, etc.
The file paper_references.md provides a complete mapping from paper sections to simulator methods, making it easy to navigate between the theory and its computational realisation.

Repository Structure

.
├── Cargo.toml              # Rust project manifest
├── src/
│   ├── lib.rs              # Main Rust library (PyO3 bindings)
│   └── shbt/               # SHBT core modules
│       ├── boundary.rs      # StaticBoundary (modular data, defect, entropy)
│       ├── entropy_flow.rs  # HolographicProjection (RG flow)
│       ├── baryogenesis.rs  # BaryogenesisOptimizer (η_B, de-rendering)
│       └── causal_point.rs  # CausalPoint (observer memory, history)
├── examples/
│   ├── run_audit.py              # Thin wrapper around shbt_simulate.py --mode audit
│   └── shbt_notebook.ipynb       # Jupyter / Colab example
├── shbt_simulate.py             # Customisable CLI/API for research runs
├── config.default.yaml          # Default simulation configuration template
├── requirements.txt             # Optional Python dependencies (plots/HDF5/pandas)
├── tests/
│   └── test_shbt.rs             # Unit tests for all SHBT components
├── main.pdf                     # The SHBT paper (formal theory)
├── paper_references.md          # Mapping from paper to code
└── README.md                    # This file

Prerequisites

Rust (1.80 or later) – install via rustup
Python (3.8 or later) – with pip
maturin (for building the Python module) – pip install maturin
(Optional) cargo‑test for running unit tests
(Optional) matplotlib, h5py, pandas for data export and plots – pip install -r requirements.txt

Build & Install

1. Build the Rust library

cargo build --release

This produces a shared library in target/release/.

2. Build the Python bindings (via maturin)

maturin build --release

This will create a wheel in target/wheels/. Install it with:

pip install target/wheels/shbt_simulator-*.whl

You can also install directly from the local folder using:

maturin develop

Now you can import the module in Python:

import shbt_simulator

Run the Full Audit

The main entry point is the Python script examples/run_audit.py (a thin wrapper around shbt_simulate.py --mode audit). It constructs a ShbtSimulator object, runs the complete audit, and prints all key results.

python examples/run_audit.py

Expected output:

{
  "branch": [26, 8, 312],
  "framing_defect (delta_fr)": 0.0,
  "modular_invariant": true,
  "zero_energy_locked": true,
  "projection_dimension_26_to_4": true,
  "eta_b": 6.449923359416e-10,
  "stress_energy_preserved": true,
  "projection_all_passed": true,
  "memory_all_passed": true,
  "metric_slices": 9,
  "history_entries": 9
}

Precision cosmology integration

The standalone module precision_cosmology.py implements the Section 9 equations and can now pull the canonical constants directly from the compiled simulator:

import precision_cosmology
constants = precision_cosmology.load_default_constants()
print(constants.h0_cmb, constants.lambda_holo_si_m2, constants.n_sat, constants.c_dark_residual)

Run its embedded audit with:

python precision_cosmology.py --run-tests

Run custom simulations with `shbt_simulate.py`

shbt_simulate.py is the unified CLI and API entry point for research runs. It wraps the Rust/PyO3 simulator and the precision_cosmology.py Section 9 audit in one interface.

python shbt_simulate.py --mode audit
python shbt_simulate.py --mode all --output result.json
python shbt_simulate.py --mode cosmology --output cosmology.json
python shbt_simulate.py --mode cosmology-test
python shbt_simulate.py --mode baryogenesis --particles 1024
python shbt_simulate.py --mode history --observer-radius-fraction 0.2

Available modes are audit (foundation audit only), cosmology (Section 9 precision-cosmology report), cosmology-test (embedded precision-cosmology unit tests), baryogenesis, history, and all (default; foundation + precision cosmology).

--mode cosmology accepts optional precision-cosmology parameters:

python shbt_simulate.py --mode cosmology \
  --h0-cmb 67.4 \
  --omega-m 0.315 \
  --omega-r0 9.2e-5 \
  --z-samples 0 0.5 1 2 10 1100 \
  --output cosmology.json

Programmatically:

import shbt_simulate
result = shbt_simulate.simulate({
    "mode": "all",
    "branch": (26, 8, 312),
    "observer_radius_fraction": 0.125,
    "redshift_max": 3.0,
    "redshift_samples": 9,
    "particles": 512,
})
print(result["audit"]["eta_b"])

Export formats

--output writes the full result to a file. The format is inferred from the extension (json, csv, h5/hdf5) or set explicitly with --format. If no format is specified, JSON is used.

python shbt_simulate.py --mode all --output result.json
python shbt_simulate.py --mode cosmology --output cosmology.json
python shbt_simulate.py --mode cosmology --format csv --output cosmology
python shbt_simulate.py --mode all --format hdf5 --output result.h5

JSON (default) stores the complete nested result tree.
CSV writes table files: {prefix}_metric_slices.csv, {prefix}_history.csv, and for cosmology {prefix}_precision_summary.csv, {prefix}_redshift_ladder.csv, {prefix}_growth_suppression.csv, {prefix}_lightcone_entropy_debt.csv, and {prefix}_isw_stability.csv.
HDF5 requires h5py and stores the full result tree.

Optional plots

If matplotlib is installed, --plot writes PNG figures alongside the data export:

python shbt_simulate.py --mode all --output result.json --plot
python shbt_simulate.py --mode cosmology --output cosmology.json --plot

For cosmology/all, this produces:

result_eigenvalues.png (metric eigenvalues vs redshift step)
result_spatial_metric.png (spatial metric heatmap)
result_hubble_ladder.png (H0(z) vs redshift)
result_growth_suppression.png (fσ8 vs redshift)
result_isw_residual.png (ISW residual vs redshift)

For sweeps, --plot also visualises the scanned parameter.

Parameter sweeps

Write a JSON file with list-valued parameters. The sweep runner detects cosmology keys (h0_cmb, omega_m, omega_r0, delta_mod, z_samples, precision) and runs the precision cosmology audit for each combination; otherwise it runs the foundation simulator.

{
  "h0_cmb": [67.0, 67.4, 68.0],
  "omega_m": [0.30, 0.315, 0.33]
}

Then run:

python shbt_simulate.py --mode cosmology --sweep sweep.json --output sweep_result.json
python shbt_simulate.py --mode cosmology --sweep sweep.json --plot --output sweep_result.json

The first command scans nine combinations of h0_cmb and omega_m. The second also writes sweep summary plots.

Foundation sweeps use the original keys:

{
  "redshift_samples": [5, 9, 13],
  "observer_radius_fraction": [0.1, 0.125]
}

Then run as before.

Configuration files

Simulation setups can be stored in YAML or JSON files and reused:

# my_config.yaml
mode: all
branch: [26, 8, 312]
observer_radius_fraction: 0.125
redshift_max: 3.0
redshift_samples: 9
particles: 512
seed: 0
# Optional precision-cosmology overrides (used when mode is cosmology or all)
h0_cmb: 67.4
omega_m: 0.315
omega_r0: 9.2e-5
z_samples: [0, 0.5, 1, 2, 10, 1100]
precision: 80
output_dir: ./simulation_results
export_formats: [json, csv]
plot: true
verbose: false
log_level: INFO
log_format: text
quiet: false

Run it with:

python shbt_simulate.py --config my_config.yaml

CLI flags override config file values, so you can iterate quickly:

python shbt_simulate.py --config my_config.yaml --mode baryogenesis --particles 1024 --seed 42

A default configuration is provided in config.default.yaml. The config is validated against a schema; if jsonschema is installed it is used, otherwise a manual validator runs.

Results are written to output_dir/<timestamp>/result.<fmt> so repeated runs are organised automatically. Each run directory also contains a reproducibility log (result.log) and result_run_info.json with the simulator version, git commit/branch (when available), config, and summary.

Use --seed or the seed config key to make Causal Point collapse selections reproducible.

Logging and output control

shbt_simulate.py uses Python's logging module. The log level, format, and output destination are configurable:

python shbt_simulate.py --mode audit --log-level DEBUG
python shbt_simulate.py --mode all --log-format json --log-file run.log
python shbt_simulate.py --mode audit --quiet
python shbt_simulate.py --mode audit --verbose

--log-level accepts DEBUG, INFO, WARNING, or ERROR (default: INFO).
--log-format json emits structured JSON lines for key events, e.g. {"event": "audit_complete", "eta_b": 6.449923359416e-10}.
--log-file appends log messages to a file as well as the console.
--quiet (-q) suppresses non-essential console output and defaults the log level to WARNING.
--verbose (-v) is an alias for --log-level DEBUG.

For audit and all modes, a clean ASCII summary table is printed at the end:

+------------------------------------------------------+
| SHBT Audit Summary                                   |
+------------------------------------------------------+
| Branch                       | (26, 8, 312)          |
| Framing defect (delta_fr)    | 0.0                   |
| Modular invariant            | True                  |
| Zero energy locked           | True                  |
| Projection dimension 26 -> 4 | True                  |
| eta_b                        | 6.449923359416131e-10 |
| Stress energy preserved      | True                  |
| Metric slices                | 9                     |
| History entries              | 9                     |
+------------------------------------------------------+

Jupyter / Colab

See examples/shbt_notebook.ipynb for a notebook that loads the simulator, runs a custom configuration, and plots the results.

Python bindings

Run all unit tests

cargo test --release

All tests should pass, confirming that the simulator satisfies the algebraic constraints derived in the paper.

Verifying the Paper’s Tables

The simulator’s audit reports (boundary_report, projection_report, memory_report, baryogenesis_identity, benchmark_delta) contain every value that appears in the paper’s tables. You can compare them directly with the printed outputs from run_audit.py.

Table 1 – Boundary closure audit → report.boundary_report
Table 2 – Holographic projection → report.projection_report
Table 3 – Causal Point memory → report.memory_report
Table 4 – Baryogenesis benchmark → report.benchmark_delta

The paper is written so that the reader can, at any point, refer to the code and see that the mathematics is implemented exactly.

Navigating the Code

Core SHBT components

Module	Structure	Paper Section
`boundary.rs`	`StaticBoundary`	Sections 2–4
`entropy_flow.rs`	`HolographicProjection`, `BulkMetricSlice`	Section 5
`baryogenesis.rs`	`BaryogenesisOptimizer`, `BaryogenesisIdentity`	Section 6
`causal_point.rs`	`CausalPoint`, `LightConeSample`, etc.	Section 7

Existing (legacy) code in `lib.rs`

The original lib.rs already implements the low‑level components used by SHBT:

High‑precision modular arithmetic (rug::Float, rug::Complex)
AnyonBraidingEngine (SU(2), SU(3), SO(10) braid matrices)
TopologicalTracker (anyon worldlines, fusion, stabiliser checks)
CircuitCompiler (Solovay‑Kitaev, OpenQASM parsing)

These are reused by the new SHBT modules where appropriate.

License

This project is licensed under the MIT License – see the LICENSE file for details.

Navigation Menu

Repository files navigation

SHBT Simulator — Static Holographic Boundary Theory

The Big Picture

Quick Start (TL;DR)

Paper ⇄ Simulator Relationship

Repository Structure

Prerequisites

Build & Install

1. Build the Rust library

2. Build the Python bindings (via maturin)

Run the Full Audit

Precision cosmology integration

Run custom simulations with `shbt_simulate.py`

Export formats

Optional plots

Parameter sweeps

Configuration files

Logging and output control

Jupyter / Colab

Python bindings

Run all unit tests

Verifying the Paper’s Tables

Navigating the Code

Core SHBT components

Existing (legacy) code in `lib.rs`

License

About

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Name		Name	Last commit message	Last commit date
Latest commit History 41 Commits
examples		examples
src		src
tests		tests
.gitignore		.gitignore
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
config.default.yaml		config.default.yaml
main.pdf		main.pdf
main.tex		main.tex
paper_references.md		paper_references.md
precision_cosmology.py		precision_cosmology.py
requirements.txt		requirements.txt
shbt_simulate.py		shbt_simulate.py
supplementary.tex		supplementary.tex

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Repository files navigation

SHBT Simulator — Static Holographic Boundary Theory

The Big Picture

Quick Start (TL;DR)

Paper ⇄ Simulator Relationship

Repository Structure

Prerequisites

Build & Install

1. Build the Rust library

2. Build the Python bindings (via maturin)

Run the Full Audit

Precision cosmology integration

Run custom simulations with shbt_simulate.py

Export formats

Optional plots

Parameter sweeps

Configuration files

Logging and output control

Jupyter / Colab

Python bindings

Run all unit tests

Verifying the Paper’s Tables

Navigating the Code

Core SHBT components

Existing (legacy) code in lib.rs

License

About

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Run custom simulations with `shbt_simulate.py`

Existing (legacy) code in `lib.rs`