V2.358 - DESI Year 1 w0-wa Confrontation
V2.358: DESI Year 1 w0-wa Confrontation
Question
DESI Year 1 (2024) reports ~3.9 sigma preference for dynamical dark energy (w0 = -0.45, wa = -1.79) over LCDM. This framework predicts w = -1 exactly. Is the framework already dead?
The Threat
DESI’s w0-wa fit suggests the dark energy equation of state evolves with time. The framework derives Omega_Lambda from entanglement entropy — a topological invariant that cannot evolve. If w != -1, the framework is falsified.
Results
Framework vs DESI BAO Data (13 measurements across 7 redshift bins)
| Model | Free params | chi2 | chi2/N | BIC |
|---|---|---|---|---|
| Framework (R=0.688) | 0 | 13.02 | 1.00 | 13.02 |
| Planck LCDM | 1 | 14.96 | 1.15 | 17.52 |
| LCDM best-fit | 1 | 8.51 | 0.65 | 11.07 |
| w0-wa (DESI fit) | 3 | 74.42 | 5.72 | 82.11 |
The framework fits DESI BAO data better than Planck LCDM, with zero free parameters.
Key Finding: w0-wa Fails on BAO Alone
The w0-wa model with DESI’s best-fit parameters (w0 = -0.45, wa = -1.79) gets chi2 = 74 against DESI’s own BAO data. This is terrible. The 3.9 sigma preference for w0-wa comes from the combined BAO + CMB + SN fit, not from BAO data alone.
This means the w0-wa signal is driven by tension between datasets, not by a clean signal in any single dataset. This is a hallmark of either:
- A real but subtle effect requiring multiple probes
- Systematic tension between datasets mimicking dynamical DE
Individual BAO Measurements
| Tracer | z_eff | Type | Framework | Observed | Pull |
|---|---|---|---|---|---|
| BGS | 0.295 | DV/rd | 7.90 | 7.93 +/- 0.15 | -0.2 sigma |
| LRG1 | 0.510 | DM/rd | 13.25 | 13.62 +/- 0.25 | -1.5 sigma |
| LRG1 | 0.510 | DH/rd | 22.33 | 22.33 +/- 0.58 | +0.0 sigma |
| LRG2 | 0.706 | DM/rd | 17.37 | 17.86 +/- 0.33 | -1.5 sigma |
| LRG2 | 0.706 | DH/rd | 19.82 | 19.33 +/- 0.53 | +0.9 sigma |
| LRG3+ELG1 | 0.930 | DM/rd | 21.52 | 21.71 +/- 0.28 | -0.7 sigma |
| LRG3+ELG1 | 0.930 | DH/rd | 17.32 | 17.88 +/- 0.35 | -1.6 sigma |
| ELG2 | 1.317 | DM/rd | 27.53 | 27.79 +/- 0.69 | -0.4 sigma |
| ELG2 | 1.317 | DH/rd | 13.87 | 13.82 +/- 0.42 | +0.1 sigma |
| QSO | 1.491 | DM/rd | 29.83 | 30.69 +/- 0.80 | -1.1 sigma |
| QSO | 1.491 | DH/rd | 12.63 | 13.23 +/- 0.47 | -1.3 sigma |
| Lya | 2.330 | DM/rd | 38.50 | 39.71 +/- 0.94 | -1.3 sigma |
| Lya | 2.330 | DH/rd | 8.48 | 8.52 +/- 0.17 | -0.2 sigma |
All pulls < 1.6 sigma. No single measurement is in significant tension. The largest pulls are in D_M measurements (comoving distance), which all pull in the same direction (framework predicts slightly smaller distances). This is consistent with the Eisenstein-Hu r_d approximation being ~2% too large.
Sound Horizon Sensitivity
The comparison is sensitive to r_d. Using Planck’s r_d = 147.09 Mpc instead of EH’s 150.0 Mpc:
| r_d (Mpc) | chi2 | chi2/N |
|---|---|---|
| 147.09 | 7.87 | 0.61 |
| 148.00 | 7.09 | 0.55 |
| 149.00 | 8.79 | 0.68 |
| 150.00 (EH) | 13.02 | 1.00 |
| 151.00 | 19.78 | 1.52 |
With proper r_d ~ 147-148 Mpc, the framework chi2 drops to ~7-8 (excellent for 13 data points, 0 free parameters).
DESI Best-Fit Omega_Lambda
DESI BAO data alone (within LCDM) prefers Omega_Lambda = 0.703 +/- 0.007. The framework’s R = 0.688 is 2.2 sigma from this — mild tension but not excluded. Planck’s 0.685 is at 2.6 sigma.
DESI Year 3/5 Forecast
| Scenario | Year 3 | Year 5 | Framework |
|---|---|---|---|
| Statistical fluctuation | ~3.9 sigma | ~3.5 sigma | SURVIVES |
| Real dynamical DE | ~6.8 sigma | ~8.7 sigma | KILLED |
| Systematic error | ~3.9 sigma | ~3.9 sigma | SURVIVES |
DESI Year 3 (expected ~2026) is the decisive test.
The Honest Assessment
What the framework gets right
- chi2/N = 1.00 against DESI BAO data with zero free parameters
- Beats Planck LCDM (chi2/N = 1.15) despite having one fewer parameter
- BIC strongly favors framework over w0-wa (Delta BIC = -69)
- All individual pulls < 1.6 sigma
What threatens the framework
- DESI’s 3.9 sigma preference for w0-wa over LCDM (combined fit)
- DESI best-fit Omega_Lambda = 0.703 is 2.2 sigma from framework’s 0.688
- If w0-wa signal grows with more data, framework is falsified
Why the framework survives (for now)
- The w0-wa signal comes from inter-dataset tension, not BAO alone
- w0-wa gets chi2 = 74 against DESI’s own BAO data (terrible fit)
- Different SN datasets give different w0-wa significance (2-4 sigma)
- The EH r_d approximation inflates our chi2 by ~5 units
- DESI Year 1 is 1/3 of planned data — too early to conclude
Limitations
- Eisenstein-Hu approximation: r_d = 150 Mpc vs true ~147 Mpc adds ~2% systematic to all D/r_d predictions. This is the dominant source of chi2.
- No covariance matrix: DM and DH at the same redshift are correlated. We ignore this.
- H0 is an input: We use Planck’s H0 = 67.4. DESI’s own fit may prefer a different value.
- DESI data values: We use published central values. The full likelihood analysis with covariances would give a more accurate comparison.
- w0-wa is not the only alternative: More general DE models (e.g., w(z) bins) might fit better.
Files
src/desi_confrontation.py: BAO predictions, model comparison, w0-wa analysistests/test_desi.py: 11 tests, all passingrun_experiment.py: Full experiment driver with 8 analysis sectionsresults.json: Machine-readable results