metadata

license: mit

🕳️ Black Hole Sim — Randomized Dataset (ProCreations)

ProCreations/black-hole-sim-randomized is a high-fidelity, randomized simulation dataset of relativistic physics near Kerr and Schwarzschild black holes. Designed to train and evaluate AI systems on general relativity, orbital mechanics, and spacetime geometry—without any visual dependencies.

📦 Dataset Overview

Samples: 400,000+
Format: JSON Lines (.jsonl)
Size: ~583 MB
Compression: Optionally .jsonl.gz
Language/Structure: Pure structured JSON (1 per line)
Generated on: Apple Silicon (Metal-accelerated)
Use cases: Pretraining, fine-tuning, RAG, reasoning, QA, simulation

✨ Features per Sample

Each sample describes a single randomly sampled scenario, containing:

🕳️ Black Hole

mass_solar – In solar masses (randomized log-uniform from 3 to 1e9)
spin – Kerr spin parameter a (0.0 to 0.998)
type – "Schwarzschild" or "Kerr"

👁️ Observer

3D position (r_km, theta_rad, phi_rad)
Orbital velocity vector in spherical coordinates
Orbital angular velocity omega

📐 Metrics (GR & physics)

time_dilation, redshift, gamma
orbital_period_s, frame_dragging, extremeness_score
metric_tensor – Full 4x4 Kerr metric in Boyer–Lindquist coordinates
a_spin_m, event_horizon_m, delta, rho_squared
Velocity vector + coordinate positions
horizon_proximity_ratio (how close the observer is to Rs)

🧪 Raw Parameters

Ground truth values like mass_kg, radius_m, rs_m

📁 Example

{
  "step": 2,
  "black_hole": {
    "mass_solar": 219.051486,
    "spin": 0.504345,
    "type": "Kerr"
  },
  "observer": {
    "r_km": 1600.060345,
    "theta_rad": 0.624667,
    "phi_rad": 4.083344,
    "v_phi_rad_s": 128884.609375
  },
  "metrics": {
    "time_dilation": 0.771804,
    "redshift": 0.295665,
    "frame_dragging": true,
    "metric_tensor": [[...]],
    "horizon_proximity_ratio": 2.654,
    "gr_metric_type": "Kerr"
  },
  "notes": "Spin=0.504, Time dilation=0.772, Redshift=0.296"
}