Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies

TL;DR

This paper combines detailed visual classifications from volunteers with deep learning to analyze the morphology of 314,000 galaxies in the DECaLS survey, revealing new features and enabling accurate automated predictions.

Contribution

It introduces a large-scale, detailed galaxy morphology dataset from volunteer classifications and trains neural networks for high-accuracy automated morphology prediction.

Findings

Deep DECaLS images reveal new galaxy features.

Neural networks achieve 99% accuracy in morphology classification.

Volunteer classifications provide a valuable resource for galaxy evolution studies.

Abstract

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r=23.6 vs. r=22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314,000 galaxies. 140,000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314,000 galaxies. When measured against confident volunteer classifications, the networks are approximately 99% accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.