Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

Garreth Martin, Sugata Kaviraj, A Hocking, S. Read, J. E. Geach

Monthly Notices of the Royal Astronomical Society 2019 article cited by 87

Source Open-access PDF More in Physics & Astronomy

importance 6/10 ai assisted Galaxies: Formation, Evolution, Phenomena astronomy stage: research

Problem

Upcoming big-data surveys will produce galaxy images at volumes that make visual inspection and supervised machine learning impractical for morphological classification.

Why it matters

Galaxy morphology underpins galaxy-evolution and cosmology analyses, so scalable classification is essential for surveys like LSST.

What the AI did

The authors apply unsupervised machine learning, which requires no training sets, to the morphological analysis of galaxies in deep-wide surveys.

Results

not stated in source

Impact

The paper presents an unsupervised approach suited to big-data survey morphology, but the abstract truncates before any results.

Record

Venue	Monthly Notices of the Royal Astronomical Society
Published	2019
Work type	article
Authors	Garreth Martin, Sugata Kaviraj, A Hocking, S. Read, J. E. Geach
Citations	87 (OpenAlex, at import time)
References	139
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1093/mnras/stz3006
Open access	bronze
Keywords	unsupervised learning, galaxy morphology, LSST, big-data surveys

Importance rating

6 / 10

significant — thousands to millions affected

Abstract calls galaxy morphology essential for galaxy-evolution studies and observational cosmology; scalable classification needed for big-data surveys like LSST.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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