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P. R. Wozniak (LANL), C. Akerlof, S. Amrose (University of Michigan), S. Brumby, D. Casperson, G. Gisler (LANL), R. Kehoe, B. Lee (University of Michigan), S. Marshall (LLNL), K. E. McGowan (LANL), T. McKay (University of Michigan), S. Perkins, W. Priedhorsky (LANL), E. Rykoff, D. A. Smith (University of Michigan), J. Theiler, W. T. Vestrand, J. Wren (LANL), ROTSE Collaboration
We evaluate several Machine Learning algorithms as potential tools for automated classification of variable stars. Using the ROTSE sample of ~1800 variables from a pilot study of 5% of the whole sky, we compare the effectiveness of a supervised technique (Support Vector Machines, SVM) versus unsupervised methods (K-means and Autoclass). There are 8 types of variables in the sample: RR Lyr AB, RR Lyr C, Delta Scuti, Cepheids, detached eclipsing binaries, contact binaries, Miras and LPVs. Preliminary results suggest a very high (~95%) efficiency of SVM in isolating a few best defined classes against the rest of the sample, and good accuracy (~70-75%) for all classes considered simultaneously. This includes some degeneracies, irreducible with the information at hand. Supervised methods naturally outperform unsupervised methods, in terms of final error rate, but unsupervised methods offer many advantages for large sets of unlabeled data. Therefore, both types of methods should be considered as promising tools for mining vast variability surveys. We project that there are more than 30,000 periodic variables in the ROTSE-I data base covering the entire local sky between V=10 and 15.5 mag. This sample size is already stretching the time capabilities of human analysts.