Dataset
Machine Learning in Astronomy Dataset
The computational layer of astronomy — the machine-learning methods, the astronomical applications (galaxy morphology, photometric redshifts, real-time alert classification), and the data-engineering workflows.
Download
Generated from the canonical knowledge graph. Open, machine-readable, and free to reuse under CC BY-SA 4.0.
- Version
- 1.0.0
- Entities
- 18
- Last generated
- 2026-06-29
- License
- CC BY-SA 4.0
- Checksum
- — (published at release)
Sample (18 of 18)
| Name | Type | ID |
|---|---|---|
| Anomaly Detection | Machine-learning method | ml_method:anomaly-detection |
| Benchmark Datasets | Data-engineering workflow | ml_workflow:benchmark-datasets |
| Classification | Machine-learning method | ml_method:classification |
| Clustering | Machine-learning method | ml_method:clustering |
| Feature Extraction | Data-engineering workflow | ml_workflow:feature-extraction |
| Foundation Models | Machine-learning method | ml_method:foundation-models |
| Galaxy Morphology Classification | Astronomical ML application | ml_application:galaxy-morphology-classification |
| Model Evaluation | Data-engineering workflow | ml_workflow:model-evaluation |
| Photometric Redshifts | Astronomical ML application | ml_application:photometric-redshifts |
| Real-Time Alert Classification | Astronomical ML application | ml_application:real-time-alert-classification |
| Regression | Machine-learning method | ml_method:regression |
| Representation Learning | Machine-learning method | ml_method:representation-learning |
| Self-Supervised Learning | Machine-learning method | ml_method:self-supervised-learning |
| Source Extraction | Astronomical ML application | ml_application:source-extraction |
| Strong Lens Finding | Astronomical ML application | ml_application:strong-lens-finding |
| Supernova Classification | Astronomical ML application | ml_application:supernova-classification |
| Training Datasets | Data-engineering workflow | ml_workflow:training-datasets |
| Transit Detection | Astronomical ML application | ml_application:transit-detection |
Source references
The primary and reference sources this topic draws on.
- NASANational Aeronautics and Space Administration
Mission data, planetary science, space telescopes, and public-domain imagery.
Most NASA-produced imagery is in the public domain; individual items are checked for usage terms before publication.
- NSF NOIRLabNSF National Optical-Infrared Astronomy Research Laboratory
Ground-based optical/infrared observatory data and imagery.