Extreme-Field QED Simulator

Status

• Stable research sandbox; actively maintained
• New: gravitational-coupling plugin (EM stress–energy → h(t), P_GW)
• YAML/JSON experiments with automated sweeps

Capabilities

• Heisenberg–Euler vacuum birefringence and photon–photon scattering (O(1) estimates)
• Schwinger pair production (order-of-magnitude)
• Linearized GR coupling: EM stress–energy → quadrupole → h(t), P_GW
• Detector models and κ-constraint “discovery engine” sweeps

See also: docs/PROVENANCE.md for reference provenance and how to add new sources.

Simulate ultra-intense light interacting with quantum vacuum structure and spacetime:

• Vacuum birefringence via Heisenberg–Euler effective Lagrangian
• Photon–photon scattering cross sections (order-of-magnitude)
• Schwinger pair production rates near the critical field
• Linearized gravitational coupling: EM stress–energy → metric perturbations
• Gravitational wave estimates: far-field strain and radiated power

This is a sandbox for "intense EM fields meet spacetime" exploration. It targets near-term, testable regimes (QED nonlinearities, vacuum polarization control) and provides a path-finder for field–spacetime interaction physics.

Quick start (reproducible)

git clone https://github.com/arcticoder/extreme-field-qed-simulator.git
cd extreme-field-qed-simulator
python -m venv .venv && source .venv/bin/activate
pip install -e .

# Minimal gravitational-coupling sanity run (tiny h_rms output)
python scripts/simulate_gravity_coupling.py --config examples/configs/interfering_pulses.json

# Expected: a small RMS strain (e.g., h_rms ~ 1e-58 to 1e-60) printed to stdout

# Optional: quick birefringence and pair-production demos
python scripts/simulate_birefringence.py --E0 1e15 --L 1.0 --lambda0 532e-9 --theta 45 --cavity-gain 1e6
python scripts/simulate_pair_production.py --E0 1e16 --volume 1e-12 --duration 1e-12

# Sweeps (save to results/sweeps/ folder)
python scripts/run_sweep.py --sweep-config examples/configs/sweep_gaussian_beam.json --output results/sweeps/gaussian_sweep.json

Features

Quantum Vacuum Physics

• Heisenberg–Euler Δn for orthogonal polarizations with arbitrary E and B
• Phase retardation and induced ellipticity for a probe beam
• Simple Schwinger-rate pair production estimator (warning: exponentially suppressed unless E ~ E_S)

Core API Functions (NEW in v2.0)

The efqs.gravitational_coupling module provides validated functions for GW emission calculations:

• quadrupole_moment(positions, energy_elements_J) — Compute Q_ij from energy distribution
• strain_far_field(Q_t, dt, R, use_tt=True, line_of_sight=None) — Far-field strain with TT projection
• radiated_power_from_quadrupole(Q_t, dt) — Instantaneous power P(t) via quadrupole formula
• dominant_frequency(series, dt, component=None) — FFT peak extraction with bandwidth
• stress_energy_from_fields(E, B, include_qed=False) — EM energy density T^{00}

TT projection details:

• Default line-of-sight: [0, 0, 1] (z-axis observer)
• Custom directions: Pass normalized 3-vector line_of_sight=[nx, ny, nz] (auto-normalized if needed)
• Projection identities: Traceless (Tr(h)=0), transverse (h·n=0), symmetric (h=h^T)
• Unit tests validate all projection properties (see tests/test_tt_projection.py)

Gravitational Coupling

• EM stress–energy tensor from E, B fields (with optional QED corrections)
• Quadrupole moment calculation from energy distributions (supports grid-based and point-source interfaces)
• Far-field gravitational strain with configurable TT projection
  • Line-of-sight selector: Specify observer direction for TT projection (default: z-axis)
  • Transverse-traceless gauge automatically enforced when use_tt=True
• Radiated GW power via quadrupole formula (returns time series P(t) or time-averaged value)
• Frequency diagnostics: Dominant frequency extraction, peak amplitude, and -3 dB bandwidth via FFT analysis
• Optional pair-production energy-loss channel in time evolution (uniform drain approximation)
• Multiple source models:
  • Interfering laser pulses (standing waves)
  • Rotating quadrupole hotspots
  • Focused Gaussian beams
  • Rotating capacitor (Biefeld-Brown-like geometry)

Infrastructure

• Installable package (pip install -e .)
• JSON/YAML configuration support
• Automated parameter sweeps
• Reusable plotting utilities
• All tests passing (5/5)

Coupling Metrics and Outputs

The gravity coupling scripts compute:

• Strain metrics: RMS strain, maximum strain, time series h_ij(t) with optional TT projection
• Power metrics: Average and peak GW power, instantaneous power time series P(t)
• Efficiency: P_GW/P_in ratio, h_rms per Joule of input energy
• Frequency spectrum: Dominant frequency (Hz), peak amplitude, -3 dB bandwidth via FFT analysis

Configuration Files

This repository supports two configuration formats:

• JSON configs (in examples/configs/): Simple format for standalone scripts like simulate_gravity_coupling.py and run_sweep.py. These directly specify parameters and support "output_json" field for saving results.
• YAML configs (in configs/): Advanced format for run_experiments.py with multi-sweep support, detector models, and anomalous coupling ansätze. Use --config configs/sweeps.yaml --sweep <sweep_name> syntax. Outputs to HDF5 by default.

Quick reference:

# JSON-based single run (saves to JSON if specified)
python scripts/simulate_gravity_coupling.py --config examples/configs/interfering_pulses.json

# JSON-based parameter sweep (saves to JSON)
python scripts/run_sweep.py --sweep-config examples/configs/sweep_gaussian_beam.json --output results.json

# YAML-based experiment pipeline (saves to HDF5)
python scripts/run_experiments.py --config configs/sweeps.yaml --sweep sweep_E0_colliding_pulses

Optional physics toggles (JSON configs):

• "include_pair_losses": true — Enable pair-production energy drain (Schwinger rate)
• "birefringence_feedback": true — First-order Δn modulation of local energy density
• "use_tt_projection": true — Apply transverse-traceless projection to strain

Physics Background

Linearized Einstein Equation

For weak gravitational fields, the metric perturbation h_μν satisfies:

$$\Box h_{\mu\nu} = -16\pi G T_{\mu\nu}$$

where T_μν is computed from EM fields. In the far-field (radiation zone), the strain is:

$$h_{ij}(t, \mathbf{R}) \approx \frac{2G}{c^4 R} \ddot{Q}_{ij}(t - R/c)$$

with proper TT projection for a given line of sight.

Radiated GW Power

The radiated gravitational wave power is:

$$P_{\text{GW}} = \frac{G}{5c^5} \langle \dddot{Q}_{ij} \dddot{Q}_{ij} \rangle$$

QED Corrections

Heisenberg–Euler energy density correction (leading order):

$$\Delta u \approx \frac{2}{45} \frac{\alpha}{\pi} \frac{1}{E_s^2} \left[ 4F^2 + 28c^2 G^2 \right]$$

where F and G are the electromagnetic field invariants.

Example Outputs

Typical run with interfering pulses at E₀ = 10¹⁵ V/m:

• RMS strain: h ~ 10⁻²² (dimensionless) at R = 10 m
• Average P_GW: ~ 10²³ W (toy configuration; not physical lab setup)

These are path-finder estimates for comparing geometries and configurations, not precision predictions.

Caution

These models are leading-order, homogeneous-field approximations. Real experiments involve spatiotemporal structure, dispersion, damage thresholds, and noise.

Discovery Engine: Systematic New Physics Search

This framework has evolved into a discovery engine for constraining new physics via null results. Instead of just computing predictions, it systematically derives coupling strength constraints (κ-bounds) when experiments see no signal.

Core Capabilities

1. κ-Parameterized Ansätze (7 physics-motivated models):

   • axion_like: E·B parity-odd coupling (compare to CAST/ADMX)
   • dilaton_like: T^μ_μ scalar-tensor gravity
   • chern_simons_like: A·B Lorentz violation (SME framework)
   • field_invariant_F2, vector_potential_squared, photon_number, spatial_gradient

2. Real Detector Models with frequency-dependent sensitivity:

   • LIGO O1, aLIGO design, LISA, Einstein Telescope
   • Quantum sensors (aspirational h~10⁻³⁰)
   • Matched-filter SNR: SNR² = 4∫|h̃(f)|²/S_n(f) df

3. Automated Parameter Sweeps:

   python scripts/run_experiments.py --config configs/sweeps.yaml --sweep sweep_E0_colliding_pulses

   • Outputs consolidated CSV with κ-constraints per ansatz–detector pair
   • Auto-generated 4-panel plots: h_rms, P_avg, κ_required, peak_freq vs. swept parameter
   • Sweep E₀, waist, Q-factor, grid resolution, etc.

4. Publication-Ready Constraint Derivation:

   • For each (source config, detector, ansatz): compute κ_required for 5σ detection
   • Null result → publish: "κ < κ_required at 95% CL"
   • Discovery reach curves: show parameter space where future experiments can probe

Quick Start: Discovery Mode

# Run a 3-point validation sweep (E₀ = 5×10¹³, 1×10¹⁴, 2×10¹⁴ V/m)
python scripts/run_experiments.py --config configs/test_mini_sweep.yaml --sweep test_mini_sweep

# Output:
# - results/sweeps/test_mini/test_mini_sweep_summary.csv (κ-constraints)
# - results/sweeps/test_mini/test_mini_sweep_plots.png (auto-plots)

# Production sweeps (7-point E₀, 6-point waist, 5-point cavity-Q)
python scripts/run_experiments.py --config configs/sweeps.yaml --sweep sweep_E0_colliding_pulses
python scripts/run_experiments.py --config configs/sweeps.yaml --sweep sweep_waist_colliding_pulses
python scripts/run_experiments.py --config configs/sweeps.yaml --sweep sweep_Q_cavity

Expected Scaling (Validation Checkpoints)

• Strain vs. Field: h ∝ E₀² (quadrupole moment ∝ stress-energy ∝ E²)
• Power vs. Field: P_GW ∝ E₀⁴ (time derivative of quadrupole)
• κ-Constraint: κ_required ∝ 1/E₀² (higher field → stronger constraint)

Example from test_mini_sweep:

E₀ = 5×10¹³ V/m  →  h_rms = 3.7×10⁻⁵⁹,  κ_LIGO = 2.3×10³³
E₀ = 2×10¹⁴ V/m  →  h_rms = 5.9×10⁻⁵⁸,  κ_LIGO = 8.9×10³⁰  (16× improvement)

Interpreting Results

Q: My κ_required is 10⁵⁰. Is the code broken?

A: No! This means the anomalous coupling would need to be enormous to produce a detectable signal with current experiments. This is useful science:

• Confirms known physics (EM + GR) predicts negligible signal
• Sets an upper bound: null result → κ < 10⁵⁰
• Guides theory: if your model predicts κ ~ 10⁴⁵, experiment is relevant; if κ ~ 10²⁰, need different approach

Q: How do I compare ansätze?

A: Don't compare κ-values across ansätze (different units, different physics). Instead:

• Compare κ-constraints within same ansatz across different experiments
• Map κ to theory-specific couplings (e.g., axion g_aγγ, dilaton VEV, SME coefficients)

Documentation & Guides

• Discovery Engine Guide – Complete methodology, ansätze catalog, sweep design, troubleshooting
• Back-Reaction Assessment Guide – How to interpret metrics and run systematic sweeps to assess EM ↔ spacetime viability
• Detector Sensitivity Curves – ASDs for LIGO/LISA/ET/quantum sensors

Documentation

• Quick Start — minimal run, expected outputs, troubleshooting
• Anomalous Coupling (κ) Modules — parameterization and units
• Discovery Engine Guide – Complete guide to κ-constraint methodology, parameter sweeps, ansätze catalog, and publication workflow
• Back-Reaction Assessment Guide – How to interpret metrics and run systematic sweeps to assess EM ↔ spacetime viability
• Provenance Guide — how references are organized and how to add new ones

License

MIT