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Register a new userGalaxy Zoo: Morphological Classifications for 120,000 Galaxies in HST Legacy Imaging
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We present the data release paper for the Galaxy Zoo: Hubble (GZH) project. This is the third phase in a large effort to measure reliable, detailed morphologies of galaxies by using crowdsourced visual classifications of colour composite images. Images in GZH were selected from various publicly-released Hubble Space Telescope Legacy programs conducted with the Advanced Camera for Surveys, with filters that probe the rest-frame optical emission from galaxies out to $z sim 1$. The bulk of the sample is selected to have $m_{I814W} < 23.5$,but goes as faint as $m_{I814W} < 26.8$ for deep images combined over 5 epochs. The median redshift of the combined samples is $z = 0.9 pm 0.6$, with a tail extending out to $z sim 4$. The GZH morphological data include measurements of both bulge- and disk-dominated galaxies, details on spiral disk structure that relate to the Hubble type, bar identification, and numerous measurements of clump identification and geometry. This paper also describes a new method for calibrating morphologies for galaxies of different luminosities and at different redshifts by using artificially-redshifted galaxy images as a baseline. The GZH catalogue contains both raw and calibrated morphological vote fractions for 119,849 galaxies, providing the largest dataset to date suitable for large-scale studies of galaxy evolution out to $z sim 1$.
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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 galaxys 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 .
We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have produced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.
Spiral structure is ubiquitous in the Universe, and the pitch angle of arms in spiral galaxies provide an important observable in efforts to discriminate between different mechanisms of spiral arm formation and evolution. In this paper, we present a hierarchical Bayesian approach to galaxy pitch angle determination, using spiral arm data obtained through the Galaxy Builder citizen science project. We present a new approach to deal with the large variations in pitch angle between different arms in a single galaxy, which obtains full posterior distributions on parameters. We make use of our pitch angles to examine previously reported links between bulge and bar strength and pitch angle, finding no correlation in our data (with a caveat that we use observational proxies for both bulge size and bar strength which differ from other work). We test a recent model for spiral arm winding, which predicts uniformity of the cotangent of pitch angle between some unknown upper and lower limits, finding our observations are consistent with this model of transient and recurrent spiral pitch angle as long as the pitch angle at which most winding spirals dissipate or disappear is larger than 10 degrees.
Rings are important and characteristic features of disc-shaped galaxies. This paper is the first in a series which re-visits galactic rings with the goals of further understanding the nature of the features and for examining their role in the secular evolution of galaxy structure. The series begins with a new sample of 3962 galaxies drawn from the Galaxy Zoo 2 citizen science database, selected because zoo volunteers recognized a ring-shaped pattern in the morphology as seen in Sloan Digital Sky Survey colour images. The galaxies are classified within the framework of the Comprehensive de Vaucouleurs revised Hubble-Sandage (CVRHS) system. It is found that zoo volunteers cued on the same kinds of ring-like features that were recognized in the 1995 Catalogue of Southern Ringed Galaxies (CSRG). This paper presents the full catalogue of morphological classifications, comparisons with other sources of classifications, and some histograms designed mainly to highlight the content of the catalogue. The advantages of the sample are its large size and the generally good quality of the images; the main disadvantage is the low physical resolution which limits the detectability of linearly small rings such as nuclear rings. The catalogue includes mainly inner and outer disc rings and lenses. Cataclysmic (encounter-driven) rings (such as ring and polar ring galaxies) are recognized in less than 1% of the sample.
We present morphological classifications of $sim$27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have $mathrm{m}_r lesssim 17.7~mathrm{mag}$; we model fainter objects to $mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97% accuracy to $mathrm{m}_r<21.5$ on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for $sim$ 87% and 73% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sersic index (textit{n}), ellipticity ($epsilon$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.
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