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Abstract

Despite the importance of knowledge of the structure of the cosmic web to our understanding of dark matter, dark energy, and the development of the universe, a satisfactory method of identifying and classifying extragalactic structures in large datasets has yet to be developed. Datasets are too large for any researcher to be expected to thoroughly examine a catalog, and existing algorithms fail to identify structures that are apparent to human classifiers. Recently,the Galaxy Zoo project has shown crowdsourced identification to be effective when applied to the classification of large catalogs of images of galaxies. We now apply the same idea to the identification and classification of extragalactic structures using data taken from the ECO and GAMA sky catalogs, presenting plotted sections of data to volunteers through Zooniverse. With this project, we show that the same crowdsourced abilities of human pattern recognition are effective in the classification of extragalactic structures, and that this method is a viable technique to be used in the future.

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Metadata

  • Subject
    • Physics & Astronomy

  • Institution
    • Dahlonega

  • Event location
    • Nesbitt 3110

  • Event date
    • 25 March 2022

  • Additional information
    • Acknowledgements:

      Amanda Moffett