Datasets:
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:
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
from datasets import load_dataset
ds = load_dataset("peijin94/PhySynthTrainer")
sample = ds["train"][0]
img = sample["image"]
labels = sample["yolo_label"]