Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey

Abstract

We present the data release for Galaxy Zoo 2 (GZ2), a citizen science project with more than 16 million morphological classifications of 304,122 galaxies drawn from the Sloan Digital Sky Survey. Morphology is a powerful probe for quantifying a galaxy's dynamical history; however, automatic classifications of morphology (either by computer analysis of images or by using other physical parameters as proxies) still have drawbacks when compared to visual inspection. The large number of images available in current surveys makes visual inspection of each galaxy impractical for individual astronomers. GZ2 uses classifications from volunteer citizen scientists to measure morphologies for all galaxies in the DR7 Legacy survey with m_r>17, in addition to deeper images from SDSS Stripe 82. While the original Galaxy Zoo project identified galaxies as early-types, late-types, or mergers, GZ2 measures finer morphological features. These include bars, bulges, and the shapes of edge-on disks, as well as quantifying the relative strengths of galactic bulges and spiral arms. This paper presents the full public data release for the project, including measures of accuracy and bias. The majority (>90%) of GZ2 classifications agree with those made by professional astronomers, especially for morphological T-types, strong bars, and arm curvature. Both the raw and reduced data products can be obtained in electronic format at http://data.galaxyzoo.org .

Reformatted URL in abstract and fixed accidental omission of Tables 6-9 in PDF version

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• Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
• The SATCHEL pipeline: A general tool for data classified through citizen science
• Passive spiral galaxies deeply captured by Subaru Hyper Suprime-Cam
• Merger or Not: Accounting for Human Biases in Identifying Galactic Merger Signatures
• Galaxy Zoo: Clump Scout: Surveying the Local Universe for Giant Star-forming Clumps
• Quantifying the Poor Purity and Completeness of Morphological Samples Selected by Galaxy Colour
• Semi-supervised learning objectives as log-likelihoods in a generative model of data curation
• Spatially Resolved Properties of Supernova Host Galaxies in SDSS-IV MaNGA
• Galaxy Morphological Classification with Efficient Vision Transformer
• Where's Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data
• Generative Adversarial Networks for Astronomical Images Generation
• A machine learning approach to estimating HI deficiency in galaxies
• Selecting Post-Starburst Galaxies Based on Star Formation History
• Analysis of the Effect of Bars on Environmental Dependence of Disc Galaxies with MaNGA Survey Data
• Identifying lopsidedness in spiral galaxies using a Deep Convolutional Neural Network
• AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
• A Search for double-lobed radio emission from Galactic Stars and Spiral Galaxies