V2.645 - SM Uniqueness from the Full BSM Landscape
V2.645: SM Uniqueness from the Full BSM Landscape
The Headline
Omega_Lambda does NOT uniquely select the SM from the full landscape.
When all discrete parameters (N_c, N_gen, n_H) are varied simultaneously, 41 theories survive within 2σ in the minimal landscape (880 total), and 358 survive in the extended landscape (6912 total). The SM is among them at +0.42σ, but it is not unique. P(coincidence) ≈ 5%, equivalent to ~2σ.
This is a critical honest result that clarifies exactly what the framework can and cannot predict.
What Omega_Lambda Encodes
| Quantity | Value |
|---|---|
| Theories in minimal landscape | 880 |
| After physics filters (anomaly, AF, EWSB) | 880 |
| Within 2σ of Omega_Lambda | 41 |
| Information content | 4.4 bits |
| P(coincidence) | ~5% (~2σ) |
The cosmological constant encodes 4.4 bits of information about particle physics — enough to reduce the landscape by a factor of ~20, but not enough to uniquely determine the theory.
Why the SM Is Not Unique (Multi-Parameter Degeneracy)
The formula R = |delta|/(6*alpha) depends on two quantities:
- delta = sum of trace anomaly coefficients (dominated by vectors and fermions)
- alpha = N_eff * alpha_s (total component count)
Higher N_c increases both delta and N_eff. Higher N_gen does the same. The RATIO delta/N_eff can be similar for many (N_c, N_gen) combinations.
Examples of non-SM theories within 1σ of Omega_Lambda:
| N_c | N_gen | n_H | grav | R | σ |
|---|---|---|---|---|---|
| 5 | 4 | 5 | no | 0.6853 | +0.09 |
| 9 | 8 | 1 | no | 0.6856 | +0.13 |
| 8 | 7 | 4 | yes | 0.6835 | -0.17 |
| 3 | 3 | 1 | yes | 0.6877 | +0.42 |
These non-SM theories require large N_c and N_gen to compensate. They are physically disfavored (no evidence for N_c > 3 or N_gen > 3) but are not excluded by Omega_Lambda alone.
The V2.641 Result in Context
V2.641 found N_c = 3 uniquely selected at 8.7σ. That was with N_gen = 3 FIXED (using the Z-width as external input). The key difference:
| Constraint set | Survivors (2σ) | SM unique? |
|---|---|---|
| Omega_Lambda alone | 41 | No |
| Omega_Lambda + N_gen = 3 (V2.641) | ~1 | Yes (8.7σ) |
| Omega_Lambda + Z-width + LEP | 1 | Yes |
Omega_Lambda is necessary but not sufficient. It must be combined with independent measurements (Z-width, LEP) to uniquely select the SM.
Parameter Sensitivity
Varying N_c (N_gen=3 fixed)
| N_c | R | σ | Exclusion |
|---|---|---|---|
| 2 | 0.6214 | -8.7σ | Excluded |
| 3 | 0.6877 | +0.4σ | SM |
| 4 | 0.7676 | +11.4σ | Excluded |
With N_gen fixed, N_c is sharply determined. V2.641’s result holds.
Varying N_gen (N_c=3 fixed)
| N_gen | R | σ | Exclusion |
|---|---|---|---|
| 2 | 0.8320 | +20.2σ | Excluded |
| 3 | 0.6877 | +0.4σ | SM |
| 4 | 0.5983 | -11.8σ | Excluded |
With N_c fixed, N_gen is sharply determined. Equally powerful.
Varying n_Higgs (N_c=3, N_gen=3 fixed)
| n_H | R | σ | Exclusion |
|---|---|---|---|
| 1 | 0.6877 | +0.4σ | SM |
| 2 (2HDM) | 0.6693 | -2.1σ | Marginal |
| 3 | 0.6519 | -4.5σ | Excluded |
2HDM is excluded at 2.1σ — marginal but meaningful. n_H ≥ 3 is excluded at >4σ.
Graviton
| Graviton | R | σ |
|---|---|---|
| Yes (n=10) | 0.6877 | +0.4σ |
| No | 0.6646 | -2.8σ |
Graviton is required at 2.8σ, consistent with V2.326/V2.328.
Extended Landscape (with singlets and vector-like fermions)
Adding 0-5 scalar singlets and 0-3 vector-like fermion pairs:
| Quantity | Value |
|---|---|
| Total theories | 6912 |
| Within 2σ | 358 |
| P(coincidence) | 5.2% (~1.9σ) |
Singlet scalars are nearly invisible to Omega_Lambda. Each singlet shifts R by only ~0.005 (delta_scalar = -1/90 is tiny compared to delta_vector = -31/45). The framework cannot exclude or confirm singlet scalars — they are in the noise.
The Non-Circular Cascade (Revised)
The correct cascade requires EXTERNAL measurements:
Step 0: Landscape (N_c, N_gen, n_H, grav) 880 theories
Step 1: Anomaly cancellation 880 (all pass)
Step 2: Asymptotic freedom 880 (all pass)
Step 3: EWSB requirement 880 (all pass)
Step 4: Omega_Lambda (2σ) 41
Step 5: Z-width (N_gen = 3) ~5
Step 6: No extra light particles (LEP) 1 (SM)
Physics filters (steps 1-3) provide NO reduction in this parameterization because all SU(N_c) × SU(2) × U(1) theories are automatically anomaly-free (V2.641) and AF for moderate N_gen.
Honest Assessment
What’s Strong
- The SM is at +0.42σ — excellent match, no tuning
- With ANY single parameter fixed (N_c or N_gen), the rest are sharply determined. The framework is predictive along each parameter axis.
- 2HDM excluded at 2.1σ, 3+ Higgs doublets at >4σ
- Graviton required at 2.8σ
- Formula R = 149√π/384 is exact (no adjustable parameters)
What’s Weak
- 41 theories survive within 2σ — the SM is not unique from Omega_Lambda alone. The multi-parameter degeneracy is real.
- P(coincidence) ≈ 5% — only ~2σ significance against “random formula matches Omega_Lambda.” Not compelling by physics standards.
- Physics filters eliminate nothing in this parameterization. Anomaly cancellation is automatic for SU(N_c) × SU(2) × U(1).
- Singlet scalars are invisible — the framework cannot distinguish SM from SM + singlet. This is a fundamental limitation (δ_scalar is too small).
- External measurements required — the Z-width and LEP data are needed to break the (N_c, N_gen) degeneracy. The framework alone doesn’t select the SM.
What This Means
V2.641’s claim “Lambda counts colors” is CORRECT but INCOMPLETE. Omega_Lambda determines N_c given N_gen, and vice versa. But it cannot determine both simultaneously without additional input.
The 4.4 bits encoded in Omega_Lambda are real and significant — the framework PREDICTS the observed value with zero free parameters. But the claim should be stated precisely:
“Omega_Lambda + Z-width → SM uniquely” (strong, correct)
NOT: “Omega_Lambda → SM uniquely” (overclaims)
The framework’s value is not that it uniquely selects the SM, but that it EXPLAINS the numerical value of the cosmological constant as a topological invariant of the SM field content. Given the SM, it predicts Omega_Lambda = 149√π/384 ≈ 0.6877, matching observation at 0.4σ.
Files
src/full_landscape.py— Theory class, landscape scanners, statisticstests/test_landscape.py— 8 verification tests (all pass)run_experiment.py— Full 8-phase experimentresults.json— Machine-readable results