4.7 Article

Galaxy Zoo: 3D-crowdsourced bar, spiral, and foreground star masks for MaNGA target galaxies

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 507, Issue 3, Pages 3923-3935

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab2282

Keywords

surveys; methods: data analysis; galaxies: bar; galaxies: spiral; galaxies: structure

Funding

  1. Global Impact Award from Google
  2. Alfred P. Sloan Foundation
  3. U.S. Department of EnergyOffice of Science
  4. centre for High-Performance Computing at the University of Utah
  5. Keck Northeast Astronomy Consortium's REU program [AST-1005024, AST-1950797]
  6. KINSC (Koshland Integrated Natural Sciences Centre) at Haverford College
  7. Ogden Trust, UK

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This paper introduces the Galaxy Zoo: 3D project, which identifies internal structures of 29,831 galaxies through crowdsourcing, providing valuable insights on the evolutionary role of non-axisymmetric processes that are often overlooked when azimuthally averaging MaNGA data cubes.
The challenge of consistent identification of internal structure in galaxies - in particular disc galaxy components like spiral arms, bars, and bulges - has hindered our ability to study the physical impact of such structure across large samples. In this paper we present Galaxy Zoo: 3D (GZ:3D) a crowdsourcing project built on the Zooniverse platform that we used to create spatial pixel (spaxel) maps that identify galaxy centres, foreground stars, galactic bars, and spiral arms for 29 831 galaxies that were potential targets of the MaNGA survey (Mapping Nearby Galaxies at Apache Point Observatory, part of the fourth phase of the Sloan Digital Sky Surveys or SDSS-IV), including nearly all of the 10 010 galaxies ultimately observed. Our crowdsourced visual identification of asymmetric internal structures provides valuable insight on the evolutionary role of non-axisymmetric processes that is otherwise lost when MaNGA data cubes are azimuthally averaged. We present the publicly available GZ:3D catalogue alongside validation tests and example use cases. These data may in the future provide a useful training set for automated identification of spiral arm features. As an illustration, we use the spiral masks in a sample of 825 galaxies to measure the enhancement of star formation spatially linked to spiral arms, which we measure to be a factor of three over the background disc, and how this enhancement increases with radius.

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