• DocumentCode
    2714119
  • Title

    Automated quantitative description of spiral galaxy arm-segment structure

  • Author

    Davis, Darren R. ; Hayes, Wayne B.

  • Author_Institution
    Univ. of California, Irvine, CA, USA
  • fYear
    2012
  • fDate
    16-21 June 2012
  • Firstpage
    1138
  • Lastpage
    1145
  • Abstract
    We describe a system that builds quantitative structural descriptions of spiral galaxies. This enables translation of sky survey images into data needed to help address fundamental astrophysical questions such as the origin of spiral structure - a phenomenon that has eluded full theoretical description despite 150 years of study. The difficulty of automated measurement is underscored by the fact that, to date, only manually-guided efforts (such as the citizen science project Galaxy Zoo) have been able to extract structural information about spiral galaxies. An automated approach is needed to eliminate measurement subjectivity and handle the otherwise-overwhelming image quantities (up to billions of images) from near-future surveys. Our approach automatically describes spiral galaxy structure as a set of arcs fit to pixel clusters, precisely characterizing spiral arm segment arrangement while retaining the flexibility needed to accommodate the observed wide variety of spiral galaxy structure. The largest existing quantitative measurements were manually-guided and encompassed fewer than 100 galaxies, while we have already applied our method to nearly 30,000 galaxies. Our output is consistent with previous information, both quantitatively over small existing samples, and qualitatively with human classifications.
  • Keywords
    astronomical image processing; galaxies; image classification; automated measurement; automated quantitative description; fundamental astrophysical questions; human classifications; manually-guided efforts; measurement subjectivity elimination; otherwise-overwhelming image quantities; pixel clusters; sky survey image translation; spiral galaxy arm-segment structure; structural information extraction; Brightness; Extraterrestrial measurements; Humans; Shape; Spirals; Transforms; Windings;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
  • Conference_Location
    Providence, RI
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4673-1226-4
  • Electronic_ISBN
    1063-6919
  • Type

    conf

  • DOI
    10.1109/CVPR.2012.6247794
  • Filename
    6247794