[4.09] Quasar Identification and Classification with Decision Trees

AAS 203rd Meeting, January 2004
Session 4 Computation, Data Handling and Image Analysis
Poster, Monday, January 5, 2004, 9:20am-6:30pm, Grand Hall

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[4.09] Quasar Identification and Classification with Decision Trees

T. Spinka, T. Carpenter, R. J. Brunner, R. Aydt, L. Auvil, T. Redman, D. Tcheng (Univ. of Illinois, Urbana)

The massive amounts of data flooding into the astronomy field hold many answers to important problems in contemporary astrophysics. The biggest problem is sifting through massive amounts of data to uncover these secrets. In this presentation, we identify an approach in which we apply data-mining techniques to the problem of photometric quasar identification. We employ decision trees to quickly and robustly identify potential quasars to a high degree of accuracy. We emphasize computational scalability due to the high volume of data and complexity of the data-mining algorithms.

The author(s) of this abstract have provided an email address for comments about the abstract: spinka@uiuc.edu

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