Hugging Face's logo

Datasets:
ProCreations
/
black-hole-sim-randomized

Modalities:
Text
Formats:
json
Size:
100K - 1M
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community
1
black-hole-sim-randomized
File size: 2,184 Bytes 
c673ee1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
 
---
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

```json
{
  "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"
}