V2.342 - Anomaly x Lambda Double Selection
V2.342: Anomaly x Lambda Double Selection
Status: (3,3) UNIQUELY SELECTED — UV anomalies fix N_c, IR Lambda fixes N_g
Central Result
The Standard Model is uniquely selected by the intersection of two completely independent constraints:
| Constraint | Physics | Selects | Mechanism |
|---|---|---|---|
| UV | Anomaly cancellation | N_c = 3 | U(1)³ = -N_c/4 + 3/4 = 0 |
| IR | Cosmological constant | N_g = 3 | R = |δ|/(6α·N_eff) = Ω_Λ |
These probe completely different physics: perturbative gauge consistency (UV) vs vacuum entanglement entropy (IR). There is no known reason they should agree. Yet they select the same theory: (N_c=3, N_g=3) = the Standard Model.
Method
Scanned 1080 models: SU(N_c) × SU(2) × U(1) with N_c=1..12, N_g=1..10, N_H=1..3, n_grav ∈ {0, 2, 10}. For each, checked:
- U(1)³ anomaly cancellation (Σ Y³ = 0)
- [SU(2)]²U(1) anomaly cancellation
- [grav]²U(1) anomaly cancellation
- Witten SU(2) anomaly (even fermion doublets)
- SU(N_c) asymptotic freedom
- R = Ω_Λ within 2σ (Planck precision)
Key Results
1. UV constraint: N_c = 3 is unique
The cubic U(1) anomaly per generation:
A(N_c) = -N_c/4 + 3/4
This is linear in N_c with unique integer zero at N_c = 3. The [SU(2)]²U(1) anomaly gives the same: N_c/6 - 1/2 = 0 → N_c = 3. The gravitational anomaly [grav]²U(1) vanishes for all N_c (automatic).
2. IR constraint: N_g = 3 is unique (at N_c = 3)
| N_g | R(3, N_g, 1, 10) | σ from Ω_Λ |
|---|---|---|
| 2 | 0.8320 | +20.2σ |
| 3 | 0.6877 | +0.4σ |
| 4 | 0.5983 | -11.8σ |
N_g = 2 and N_g = 4 are excluded at >10σ. Only N_g = 3 matches.
3. Full scan: 2 out of 1080 survive UV ∩ IR
| Filter | Surviving | Fraction |
|---|---|---|
| None | 1080 | 100% |
| Anomaly-free only | 90 | 8.3% |
| Lambda match (2σ) only | 50 | 4.6% |
| UV ∩ IR | 2 | 0.19% |
The 2 survivors:
- (3, 3, 1, 10): R = 0.6877, σ = +0.4 — the SM
- (3, 3, 3, 2): R = 0.6927, σ = +1.1 — 3 Higgs doublets, 2 graviton modes
The SM is the best match. The competitor requires 3 Higgs doublets (not observed) and only 2 graviton modes (contradicts V2.337 derivation of n=10).
4. Information content
| Stage | Surviving | H (bits) | Gained |
|---|---|---|---|
| Prior | 1080 | 10.1 | — |
| After UV | 90 | 6.5 | 3.6 bits |
| After IR | 50 | 5.6 | 4.4 bits |
| After UV ∩ IR | 2 | 1.0 | 9.1 bits |
UV and IR are complementary: UV constrains N_c (not N_g), IR constrains the N_g-N_c relationship. Together they provide 9.1 bits — nearly complete identification from 10.1 bits of prior uncertainty.
5. The Double Selection Matrix
Ng=1 Ng=2 Ng=3 Ng=4 Ng=5 ...
Nc=1 · · · · ·
Nc=2 · · · · ·
Nc=3 UV UV ★★★ UV UV ...
Nc=4 · · · · ·
...
Nc=8 · · · · ·
Anomaly cancellation fills the N_c=3 row (UV). Lambda matching scatters across the space (IR). The unique intersection ★★★ is at (3,3) = the SM.
Why This Matters
-
No parameter adjustment: Both constraints are zero-parameter. The anomaly condition is exact algebra; the Lambda prediction uses only field counting.
-
Complementary information: UV tells us N_c but nothing about N_g. IR tells us N_g (given N_c) but can’t independently determine N_c. Neither alone identifies the SM; both together do.
-
Independent physics: Anomaly cancellation follows from the gauge structure of quantum field theory. Lambda follows from vacuum entanglement entropy. These are not logically connected — their agreement is non-trivial.
-
Falsifiable: If a 4th generation or extra gauge bosons are discovered, the IR constraint fails. If the hypercharge assignments differ from the standard pattern, the UV constraint changes. Either would break the double selection.
Caveats
- The UV constraint assumes the standard hypercharge assignment. Other assignments could allow different N_c values.
- The second survivor (3,3,3,2) is distinguishable from the SM only via N_H and n_grav, not via Ω_Λ alone (1.1σ vs 0.4σ).
- Asymptotic freedom does not further reduce the UV ∩ IR set (both survivors are AF).
Files
src/double_selection.py: Anomaly computation, Lambda prediction, model scanrun_experiment.py: Full 10-section analysis with selection matrixtests/test_double_selection.py: 18 unit tests (all passing)