Artificial Neural Network Classification of Asteroids in the Sloan Digital Sky Survey

Details Artificial Neural Network Classification of Asteroids in the Sloan Digital Sky Survey Artificial Neural Network Classification of Asteroids in the Sloan Digital Sky Survey

Sep 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008dps....40.6003m&link_type=abstract

American Astronomical Society, DPS meeting #40, #60.03; Bulletin of the American Astronomical Society, Vol. 40, p.508

Astronomy and Astrophysics

Astronomy

1

Scientific paper

There are currently only a few thousand asteroids with known classifications. Our aim is to increase this number to over 20,000 by classifying asteroids identified in the Sloan Digital Sky Suvey (SDSS) Moving Object Catalogue using an artificial neural network that has been developed using the Neural Network Toolbox in Matlab. With this neural network, we are able to provide classifications for 22,847 asteroids based on normalized relfectances derived from the g', r', i', and z' SDSS magnitudes. The neural network was trained using a combination of previously classified asteroids, asteroids from known dynamical families, and asteroids we classified by hand from the SDSS reflectances. The previously classified asteroids were from the Small Main-Belt Asteroid Spectroscopic Survey (SMASS) and the Small Solar System Objects Spectroscopic Survey (S3OS2). Asteroids were divided into 13 spectral classes (T, D, B, C, X, K, S, L, A, R, Q, V and O), based on the previous taxonomies of Tholen (1984) and Bus and Binzel (2002). A major advantage of the neural network approach is that it generates a set of possible classifications for each asteroid, along with associated probabilities that emulate the continuum between classes observed in asteroid taxonomy. Our neural network solution can be applied to any new asteroid observations made in the g', r', i', z' system. We anticipate that this network and any supporting algorithms will be made publicly available in the near future via the world wide web. We will present a description of this artificial neural network and the resulting classifications as well as a discussion of its accuracy and limitations. This work was conducted through a Research Experience for Undergradutes (REU) position at the University of Hawaii's Institute for Astronomy, funded by the NSF.

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