Galaxy Zoo: Clump Scout – Design and first application of a two-dimensional aggregation tool for citizen science
Abstract
Galaxy Zoo: Clump Scout is a web-based citizen science project designed to identify and spatially locate giant star forming clumps in galaxies that were imaged by the Sloan Digital Sky Survey Legacy Survey. We present a statistically driven software framework that is designed to aggregate two-dimensional annotations of clump locations provided by multiple independent Galaxy Zoo: Clump Scout volunteers and generate a consensus label that identifies the locations of probable clumps within each galaxy. The statistical model our framework is based on allows us to assign false-positive probabilities to each of the clumps we identify, to estimate the skill levels of each of the volunteers who contribute to Galaxy Zoo: Clump Scout and also to quantitatively assess the reliability of the consensus labels that are derived for each subject. We apply our framework to a data set containing 3561 454 two-dimensional points, which constitute 1739 259 annotations of 85 286 distinct subjects provided by 20 999 volunteers. Using this data set, we identify 128 100 potential clumps distributed among 44 126 galaxies. This data set can be used to study the prevalence and demographics of giant star forming clumps in low-redshift galaxies. The code for our aggregation software framework is publicly available at: https://github.com/ou-astrophysics/BoxAggregator
Additional Information
HD and SS were partly supported by the ESCAPE project; ESCAPE – The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement no. 824064. SS also thanks the Science and Technology Facilities Council for financial support under grant ST/P000584/1. MW gratefully acknowledges support from the Alan Turing Institute, grant reference EP/V030302/1. This research is partially supported by the National Science Foundation under grants AST 1716602 and IIS 2006894.. This material is based upon work supported by the National Aeronautics and Space Administration (NASA) under Grant No. HST-AR-15792.002-A. This publication uses data generated via the Zooniverse.org platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation. This research made use of the open-source PYTHON scientific computing ecosystem, including NUMPY (Harris et al. 2020), MATPLOTLIB (Hunter 2007), and PANDAS (McKinney 2010). This research made use of Astropy, a community-developed core PYTHON package for Astronomy (The Astropy Collaboration et al. 2018). This research made use of NUMBA (Lam, Pitrou & Seibert 2015). DATA AVAILABILITY. The data underlying this article were used in Adams et al. (2022) and can be obtained as a machine-readable table by downloading the associated article data from https://doi.org/10.3847/1538-4357/ac6512.Additional details
- Eprint ID
- 118253
- Resolver ID
- CaltechAUTHORS:20221206-901120000.2
- 824064
- European Research Council (ERC)
- ST/P000584/1
- Science and Technology Facilities Council (STFC)
- EP/V030302/1
- Engineering and Physical Sciences Research Council (EPSRC)
- AST-1716602
- NSF
- IIS-2006894
- NSF
- HST-AR-15792.002-A
- NASA
- Alfred P. Sloan Foundation
- Created
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2023-01-06Created from EPrint's datestamp field
- Updated
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2023-01-06Created from EPrint's last_modified field