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
Link To Document