---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: yolo_label
    dtype: string
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 1518865822
    num_examples: 42500
  - name: validation
    num_bytes: 179666087
    num_examples: 5000
  - name: test
    num_bytes: 89229452
    num_examples: 2500
  download_size: 1657455551
  dataset_size: 1787761361
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: apache-2.0
tags:
- physics
- solar-physics
- radio-astronomy
- event-detection
- yolo
size_categories:
- 10K<n<100K
---


# Physics-based Radio Burst Training-set Event Detection

This dataset contains **50,000 radio dynamic spectrum images** produced using a **physics-informed solar radio burst simulator**, designed for training modern computer vision models (e.g., YOLO, CNNs, ViTs, diffusion models) to detect **fine-scale radio burst features** at low frequencies.

It includes **1,092,982 labeled events**, spanning Type III / Type IIIb / spike-like and noise storm structures.  
The simulations follow realistic flux, duration, bandwidth, drift rate, and turbulence-informed morphology distributions derived from observations at LOFAR and OVRO-LWA.

---

## 📊 Flux Density Distribution

The simulated event amplitudes follow a **power-law distribution** consistent with solar radio burst statistics:

<img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F6914eb6cb0eda273f948014c%2FelaCFZyeG8zYymkiiDsKl.png" width="700"/>


This ensures realistic class imbalance and naturally high occurrence of weaker bursts.

---

## Physics Background

The simulator uses:

- Plasma emission physics for fundamental/harmonic radiation  
- Density models for frequency–height mapping  
- Turbulence-based fine-structure generation  
- Drift-rate distributions from empirical solar burst measurements  
- Stochastic modeling for multi-lane and fragmented bursts  

The goal is to **bridge physics-based generative models with machine learning detection tasks**.

---

## Intended Use Cases

- Solar radio burst detection (YOLO, CNNs, transformers)  
- Real-time space weather event classifiers  
- Benchmarking ML models on fine-structure detection  
- Data augmentation for low-frequency radio solar instruments  
- Cross-instrument generalization (e.g., LOFAR → OVRO-LWA → SKA-Low)

---

## Loading the Dataset in Python

```python
from datasets import load_dataset

ds = load_dataset("peijin94/PhySynthTrainer")

sample = ds["train"][0]
img = sample["image"]
labels = sample["yolo_label"]
```