astroML/astroML

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1200 stars · 322 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AstroML is a Python library for machine learning, statistics, and data mining aimed at astronomy and astrophysics, built on numpy, scipy, scikit-learn, matplotlib, and astropy. It provides statistical/ML routines, loaders for open astronomical datasets, and a large suite of analysis and visualization examples accompanying the textbook 'Statistics, Data Mining, and Machine Learning in Astronomy'.

Use cases

• apply machine learning to astronomical survey data
• load and analyze open astronomy datasets in Python
• compute astronomical statistics like periodograms and correlation functions
• classify stars and galaxies with scikit-learn workflows
• learn ML for astronomy from textbook examples
• visualize astronomical data distributions

When to choose

• you are doing statistical or ML analysis of astronomical data in Python
• you want dataset loaders and worked examples tied to the Ivezic et al. astronomy textbook
• you want a pure-Python, BSD-licensed library that integrates with scikit-learn and astropy

When to avoid

• you need general-purpose ML outside astronomy (use scikit-learn directly)
• you need cutting-edge deep learning tooling (use PyTorch/TensorFlow ecosystems)
• you need actively developed features or rapid bug-fix turnaround

Facets

library · maturity maintenance

machine-learning data-science data-visualization etl astronomy machine-learning data-science python cross-platform astronomy astrophysics scikit-learn statistics data-mining scientific-computing textbook-companion

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