# astroML/astroML

Machine learning, statistics, and data mining for astronomy and astrophysics

Repository: https://github.com/astroML/astroML
Canonical: https://ross.abutalabs.com/products/astroml
Homepage: https://www.astroml.org/
Language: Python
License: BSD-2-Clause
License Family: permissive
Last push: 2024-05-25T09:25:40+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 5068, "days_push": 830, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1200, forks 322 (observed 2026-08-28T04:03:58.039811+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science, data-visualization, etl
- domain: astronomy, machine-learning, data-science
- platform: python, cross-platform
- tags: astronomy, astrophysics, scikit-learn, statistics, data-mining, scientific-computing, textbook-companion

## Member repositories
- astroML/astroML (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:58.039811+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:20:46.277005+00:00, confidence not recorded.
  - readme: https://github.com/astroML/astroML (fetched 2026-08-28T04:03:58.039811+00:00, sha 828b6773e90b)
  - homepage: https://www.astroml.org/ (fetched 2026-08-29T12:28:15.518888+00:00, sha 86ccfac025b0)
  - site_page: https://www.astroml.org/user_guide/installation.html (fetched 2026-08-29T12:28:15.527916+00:00, sha d02e2462699c)
- Data as of 2026-08-30T08:39:29.467469+00:00.