Note to users. If you're seeing this message, it means that your browser cannot find this page's style/presentation instructions -- or possibly that you are using a browser that does not support current Web standards. Find out more about why this message is appearing, and what you can do to make your experience of our site the best it can be.

Science 10 February 1995:
Vol. 267. no. 5199, pp. 859 - 862
DOI: 10.1126/science.267.5199.859
Prev | Table of Contents | Next

Articles

Galaxies, Human Eyes, and Artificial Neural Networks

O. Lahav 1, A. Naim 1, R. J. Buta 2, H. G. Corwin 3, G. de Vaucouleurs 4, A. Dressler 5, J. P. Huchra 6, S. van den Bergh 7, S. Raychaudhury 6, L. Sodré Jr. 8, and M. C. Storrie-Lombardi 1

1 Institute of Astronomy, Madingley Road, Cambridge CB3 OHA, UK.
2 Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487-0324, USA.
3 California Institute of Technology, IPAC M/S 100, Pasadena, CA 91125, USA.
4 Department of Astronomy, RLM 15.308, University of Texas, Austin, TX 78712-1083, USA.
5 Carnegie Observatories, 813 Santa Barbara Street, Pasadena, CA 91101-1292, USA.
6 Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA.
7 Dominion Astrophysical Observatory, 5071 West Saanich Road, Victoria, British Columbia, V8X 4M6, Canada
8 Instituto Astronômico e Geofisico da Universidade de São Paulo, CP9638, 01065, São Paulo, Brazil

The quantitative morphological classification of galaxies is important for understanding the origin of type frequency and correlations with environment. However, galaxy morphological classification is still mainly done visually by dedicated individuals, in the spirit of Hubble's original scheme and its modifications. The rapid increase in data on galaxy images at low and high redshift calls for a re-examination of the classification schemes and for automatic methods. Here are shown results from a systematic comparison of the dispersion among human experts classifying a uniformly selected sample of more than 800 digitized galaxy images. These galaxy images were then classified by six of the authors independently. The human classifications are compared with each other and with an automatic classification by an artificial neural network, which replicates the classification by a human expert to the same degree of agreement as that between two human experts.

Submitted on August 16, 1994
Accepted on December 1, 1994


THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
Galaxy Experts Train Electronic Stand-Ins.
J. Travis (1995)
Science 267, 792
   PDF »


To Advertise     Find Products

Science. ISSN 0036-8075 (print), 1095-9203 (online)