SDGFT Oracle Database: High-Resolution Parameter-Observable Lattice for Scale-Dependent Gravitational Field Theory

Overview

This dataset provides the complete pre-computed oracle databases for the Scale-Dependent Gravitational Field Theory (SDGFT) Machine-Learning Toolkit (sdgft-ml-toolkit). The two Parquet files encode a dense, high-resolution mapping from the two free SDGFT parameters (Δ, δ_g) to 37 physical observables spanning cosmology, particle physics, gravitational-wave astronomy, and astrophysics.

These files serve as the ground-truth reference for the GNN surrogate model, the CVAE inverter, and the experimental-validation pipeline described in the accompanying code repository.

Files

Parameter Space

The SDGFT is a two-parameter extension of General Relativity that promotes Newton's constant to a scale-dependent running coupling G(k).

• Anomalous dimension (Δ): [0.01, 0.50] — Controls the power-law running of G(k) in the deep UV.
• Graviton mass gap (δ_g): [0.001, 0.100] — Dimensionless IR deformation parameter. Controls late-universe deviations from ΛCDM.

Axiom Point: (Δ* = 0.2083, δ_g* = 0.0417) is the unique fixed point where all 37 observables simultaneously agree with experimental data within 2σ.

Observable Catalogue (37 columns)

1. Cosmological Observables (10)

• H0, Omega_m, Omega_Lambda, Omega_k, sigma_8, n_s, r_tensor, S_8, z_eq, t_universe

2. Particle Physics Observables (8)

• m_higgs, m_top, m_W, m_Z, alpha_s_MZ, sin2_theta_W, m_electron, alpha_em_MZ

3. Gravitational-Wave Observables (7)

• f_gw_peak, Omega_gw, h_c_nHz, dephasing_BBH, delta_v_gw, f_ring_BH, tau_ring_BH

4. Astrophysical Observables (7)

• M_TOV, R_14, Lambda_tidal, v_rot_flat, M_BH_shadow, Gamma_PPN, Beta_PPN

5. Quantum-Gravity Signatures (5)

• d_spectral, S_BH_correction, E_trans_LIV, tau_proton, G_Newton_eff

Data Format & Usage

Format: Apache Parquet (v2.6), Snappy compression. All values are stored as float64.

import pandas as pd

# Load the database
df = pd.read_parquet("oracle_db.parquet")

# Access axiom point
axiom = df.query("abs(Delta - 0.2083) < 0.0005 and abs(delta_g - 0.0417) < 0.00005")

Relation to Code Repository

These data files are consumed by the sdgft-ml-toolkit Python package for GNN training and CVAE inversion.

Repository: https://github.com/cosmologicmind/sdgft-ml-toolkit