Code & verification

Code, data analyses, and verification scripts

Plain Python sources behind the falsification tests, the predictions table, and the empirical status. Each file is a self-contained analysis script; download, read, run.

the canonical script set is now under corpus. Aggregate verifier verification script; particle-physics pack particle-physics verification; cosmology supplementary pack cosmology verification; 600-cell identity verifier 600-cell identity verification; axis re-derivation; Bayesian recompute (13.1σ / 8.3σ / 0σ provisional via Δχ² attribution).
These scripts implement the analyses behind the published predictions and falsification tests. They are intended for reproduction and inspection, not as a packaged library. Each script has its own dependencies (commonly NumPy, SciPy, and either Matplotlib or pandas); read the imports at the top of each file before running.
Amendment, BAO geometry revision, and KiDS S_8 audit reclassification (, later same day). Eight scripts (cosmology_tests.py, audit_fails.py, cmb_analysis.py, dct_desi_bao_test.py, dct_gc_age_resolution.py, dct_eddington_ratio_per_z.py, cosmological_tensions_analysis.py, pantheon_supernova_analysis.py) retain labelled branches from an interim Avrami profile \(P(z) = P_0 + (1-P_0)\exp(-t(z)/t_{50})\) (2026-05-04). That profile is not corpus-canonical at homogeneous cosmological scales; evolution in \(P\) that is derived in the public action is spatial (\(P(g)\), \(P(\Phi)\)). Separately, homogeneous \(P(t)\) cancels from radial null \(\chi(z)\); the large DESI \(\Delta\chi^2\) from rescaling \(D_M\) is an obsolete distance map (see /observables/bao/, dct_desi_bao_test.py header). The KiDS-Legacy 2025 \(S_8\) "FAIL" was reclassified PASS in review: the canonical \(c_{GW}=c\) constraint (RESOLVED, "under \(c_{GW}=c\) the disformal contribution is 8+ orders too small to affect \(S_8\)") is binding, so the canonical \(\sigma_8 \approx 0.811 \to S_8 \approx 0.831\), giving a 0.89\(\sigma\) match to KiDS-Legacy 0.815. Globular-cluster ages remain \(\sim\!2.9\)–\(4.0\sigma\) tension vs HD140283 under canonical constant-\(P_0\) (no comparable audit resolution found). Any Avrami \(P(z)\) branch in these files is diagnostic only. See /updates/ and /observables/t-univ-avrami/.

δ_CP — loop-running estimate

Computes the SM leading-log running window 7°–12° that brackets the residual on δ_CP = π/3. Backs: delta-cp · predictions row.

delta_CP_loop_running.py 4 KB Download Python source

m_p / m_e — chance-coincidence Monte Carlo

Brute-force ~51 M algebraic-formula search; computes P_chance = 2.6 × 10⁻⁵ → 4.6σ. Backs: m-p-m-e · structural identity #1.

proton_electron_mass_ratio_chance.py 5 KB Download Python source

Partition function — S(β = 0.966) = ln(31)

Verifies the Casimir-invariant correction to the partition function: N_eff = 31.005 exact. Backs: s-beta · master-identity.

partition_function_verification.py 3 KB Download Python source

SPARC galaxy fits — radial-acceleration relation

Fits the Avrami profile P(g) = 1 − exp(−√(g/g_†)) against 175 SPARC galaxies; mean χ²/N = 0.97. Backs: g-dagger · DCT-DM-01.

sparc_galaxy_rotation_curves.py 14 KB Download Python source

Cosmology suite — H(z), CC, growth, BAO

Compares DCT predictions vs ΛCDM across cosmic chronometers, BAO, growth, and structure. Backs: cc-hz · s-8 · h-phys · DCT-COS-01.

cosmology_tests.py 20 KB Download Python source

CMB — Planck PR3 acoustic and lensing

Extracts A_L, peak ratios, damping tail; checks the conformal-wall-invariance prediction. Backs: delta-n-eff · eta-baryon · n-s-spectral-index · DCT-CMB-01.

cmb_analysis.py 24 KB Download Python source

Solar-system PPN, LLR, Cassini, lensing

Checks γ, β, Nordtvedt η; sets up the BepiColombo MORE 2028 prediction. Backs: gamma-ppn · beta-ppn · nordtvedt-eta · cassini-gamma · DCT-PPN-01.

gravity_tests.py 20 KB Download Python source

Pantheon+ supernovae — w(z), Path I exclusion

Fits SNIa data; returns w_0 = -1.00, |w_a| < 10⁻⁴, and excludes Path I (ε_0 = 0.063) at 19σ. Backs: w-0 · w-a · DCT-COS-01.

pantheon_supernova_analysis.py 15 KB Download Python source

LIGO — gravitational-wave events

Tests c_GW = c (GW170817 + 8-event suite) and DCT-modified ringdown predictions. Backs: hawking-ratio · DCT-SBD-01.

ligo_gravitational_wave_analysis.py 25 KB Download Python source

Particle physics — Standard Model derivations

Mass ratios, mixing angles, gauge-coupling running across the McKay 2I → E_8 derivation. Backs: sin-theta-13 · inv-alpha · m-tau-m-mu · CKM · jarlskog-j · DCT-SM-01.

particle_physics_tests.py 46 KB Download Python source

Muon g − 2

Compares Fermilab g-2 measurement against DCT prediction. Backs: muon g-2 forward prediction.

muon_g_minus_2_analysis.py 31 KB Download Python source

Cosmological tensions — H₀, S₈, w(z)

Joint Bayesian preference vs ΛCDM, recomputed after KiDS-Legacy 2025 + DESI DR2. Backs: joint Bayesian table · 17-method H₀ correlation.

cosmological_tensions_analysis.py 19 KB Download Python source

Galaxy survey — mock-pipeline validation

Pipeline-validation script using synthetic seeded-RNG data (not real BOSS/SDSS/DES extracts). Reserved for sanity-checking the f(σ_8) and large-scale-structure analysis pipeline. Real-data analysis is in DCT-COS-01.

galaxy_survey_mega_analysis.py 12 KB Download Python source

Astrophysical tests

Stellar/planetary, cluster, and high-energy-astrophysics consistency checks. Backs: m-lens-m-dyn · hawking-ratio · wald-entropy · DCT-DM-01.

astrophysical_tests.py 32 KB Download Python source

Temporal \(P(z)\) diagnostics (non-corpus) and BEC-reality scripts

Scripts that still contain a reverted temporal Avrami \(P(z)\) branch (2026-05-04 interim, amendment) for late-universe observables, plus the BEC reality test. Corpus-canonical cosmology holds \(P = P_0\) since BBN; read each script header before citing numbers.

BEC reality test — log₁₀(BF) = +4.34

Five-sub-test cross-domain validation that the DCT vacuum's parameters extracted from gravity, quantum, and time domains satisfy mean-field BEC consistency relations \(c_s^2 = \mu/m_{\rm BEC}\), \(\xi = \hbar/\sqrt{2 m \mu}\), \(a_\dagger = c_s^2/\xi\). Backs /predictions/#bec-reality-test and /tests/#bec-reality-test.

bec_reality_test_v3.py Download Python source

GC ages — constant-\(P_0\) branch + reverted Avrami diagnostic

Integrates the Friedmann equation: headline corpus path gives \(t_{\rm univ} \approx 12.7\) Gyr (tension vs HD140283); the script retains an explicit non-canonical Avrami-\(P(z)\) branch (\(\sim\)13.7 Gyr) for reproducibility only. Backs /observables/t-univ-avrami/.

dct_gc_age_resolution.py Download Python source

Cosmic chronometer per-z — honest negative

Per-redshift comparison \(H_{\rm obs}(z)/H_{\Lambda{\rm CDM}}(z)\) on the Moresco compilation. Weighted mean ratio 1.0143 ± 0.0227 → 0.63σ from ΛCDM, 3.08σ from naive uniform-multiplier BEC. The simple BEC form is rejected; a low-\(z\) transition in an \emph{effective} expansion rate is one possible direction — not to be read as restoring temporal \(P(z)\) as corpus-canonical. Backs /tests/#cosmic-chronometer-per-z.

dct_eddington_ratio_per_z.py Download Python source

EDGES 21-cm — DCT inactive at z = 17

At z = 17 the BEC is uncondensed (P → 1) under the Avrami profile, so DCT does not modify the 21-cm absorption signal in either the EDGES or SARAS-3 direction. Status neutral. Backs /tests/#edges-21cm.

dct_edges_21cm.py Download Python source

NANOGrav 15-yr γ_PTA — neutral

Measured γ_PTA = 3.2 ± 0.6. SMBHB γ = 13/3 = 4.33; both DCT and SMBHB sit at ≈ 1.9σ from the central value. DCT's specific contribution at scalar-mode frequency 7.6 × 10⁻²⁰ Hz is below NANOGrav's 1/yr sensitivity. Backs /tests/#nanograv-15yr-gamma.

dct_nanograv_spectrum.py Download Python source

FAIL audit — 33-test post-audit count

Summary script tabulating the 33-facet survey after the BAO geometry revision: 20 PASS / 13 NEUTRAL / 0 FAIL (run audit_fails.py for the live printout). Row-by-row registry with ΛCDM / GR / SM null baselines and DCT contrasts: /empirical/facets/ (facet_registry.json). Backs /scorecard/#33-facet-pass-neutral-fail.

audit_fails.py Download Python source

DESI BAO — legacy χ² branches + geometry note

DESI Year-1 12-bin harness: reproduces legacy \(\Delta\chi^2\) on the obsolete \(D_M\) rescaling branch and optional diagnostic Avrami \(P(z)\) branches. Read the script header before citing numbers. Backs /observables/bao/.

dct_desi_bao_test.py Download Python source

A formal Git repository will be made available alongside arXiv submission. Until then, these self-contained scripts are the authoritative public record of the analysis code.

Input data

Public archives the verification scripts pull from. Each entry shows the source, the typical retrieval, and the cluster-paper analysis that uses it.

ArchiveSourceUsed by
GWOSC GWTC-1+2.1+3 strain HDF5gwosc.orgHF pixelation null check
PTB Yb⁺/Sr clock fractional frequencyPTB-OAR DOI 10.7795/720.20210126Lomb–Scargle / diurnal modulation
NANOGrav 15-yr pulsar timingNANOGrav Collaboration ZenodoBrans–Dicke ω₀² dipole bound
Euclid Q1 strong-lens catalogueWalmsley et al. 2025 ZenodoM_lens / M_dyn turnover (E29) first-look
King 2012 quasar α-dipole rawKing et al. 2012 supplementarySpatial-α gradient reproduction
SPARC galaxy rotation curvesLelli–McGaugh–Schombert 2016Avrami profile / RAR fit
Cosmic chronometers H(z)Moresco compilationPer-z H_obs/H_LCDM ratio
Multi-method H₀ panel17 published distance methodsH₀ vs condensate-density correlation
NIST Atomic Spectra DatabaseNIST ASD v5.11Conformal-wall theorem invariance
CODATA 2018 fundamental constantsCODATAStructural-identity chance probability
PDG 2024Particle Data GroupCKM, masses, mixing angles
DESI Y1 BAODESI Collaboration 2024D_M(z)/r_d under constant-P branch
KiDS-Legacy 2025Wright et al. 2025S_8 data update
Lange 2021 LPIPTB-OARLocal position-invariance reproduction
Cassini Doppler boundBertotti, Iess, Tortora 2003PPN γ bound
BepiColombo MORE projectionIess, Asmar, Tortora 20212028 forward decisive test forecast

How to reproduce a numerical claim

  1. Pick a row from the predictions table or a result on the empirical evidence page.
  2. Open the cluster paper named in the source column under /papers/; the per-prediction derivation chain is in the body of that paper.
  3. Pull the corresponding public-archive dataset from the table above.
  4. Run the verification script listed below; the script consumes the public archive and reproduces the prediction-vs-measurement comparison without private data.