V2.84 Thermodynamic Uniqueness COMPLETE
DHOST Stress Test
Experiment V2.84: DHOST Stress Test
Status: COMPLETE
Goal
Stress-test the thermodynamic equilibrium criterion against the full DHOST theory space. Scan the DHOST parameter space exhaustively and verify the exclusion fraction under N > N_threshold.
Key Result
99.2% of DHOST parameter space excluded by the thermodynamic prior N < 10^{-3}. The surviving 0.8% are theories very close to GR (small deviations in F_2, A_1).
Method
- Parameterize DHOST theories via (F_2, A_1, A_2, A_3) functions
- Scan over 10^4 parameter points with varying functional forms
- For each point, compute N over cosmic history using FRW backgrounds
- Apply threshold and compute exclusion fraction
Results
| DHOST Class | Points Sampled | Excluded (N > 10^{-3}) | Surviving |
|---|---|---|---|
| Quadratic DHOST | 3000 | 99.4% | 0.6% |
| Cubic DHOST | 3000 | 99.1% | 0.9% |
| Beyond-Horndeski | 2000 | 98.8% | 1.2% |
| General c_T=1 | 2000 | 99.5% | 0.5% |
| Total | 10000 | 99.2% | 0.8% |
Non-Trivial Structure
The surviving theories are not random — they cluster near the GR fixed point with specific structure:
- F_2 deviations < 10^{-3} of Planck mass
- A_1 coefficients < 10^{-4}
- All observational predictions within current bounds
Observational Predictions
The excluded theories would produce detectable signatures:
- Modified gravitational wave propagation (alpha_T != 0)
- Scale-dependent growth of structure
- Modified ISW effect in CMB
Modules
| Module | Purpose |
|---|---|
dhost_models.py | DHOST model definitions |
dhost_backgrounds.py | DHOST background solutions |
dhost_thermodynamics.py | Thermodynamic quantities for DHOST |
dhost_eft_mapping.py | DHOST to EFT parameter mapping |
non_equilibrium_N.py | N measure computation |
non_trivial_structure.py | Non-trivial structure analysis |
observational_constraints.py | Observational constraints |
observational_predictions.py | Observable predictions |
parameter_scanning.py | Parameter space scanning |
results_analysis.py | Results analysis and visualization |
threshold_derivation.py | Threshold derivation |
common.py | Shared utilities |