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Supernova Recognition using Support Vector Machines Raquel A. Romano Cecilia R. Aragon Chris Ding
 

Summary: Supernova Recognition using Support Vector Machines
Raquel A. Romano Cecilia R. Aragon Chris Ding
Computational Research Division
Lawrence Berkeley National Laboratory
1 Cyclotron Road, Berkeley, CA 94720
E-mail: {romano,aragon,chqding}@hpcrd.lbl.gov
Abstract
We introduce a novel application of Support Vector Ma-
chines (SVMs) to the problem of identifying potential su-
pernovae using photometric and geometric features com-
puted from astronomical imagery. The challenges of this
supervised learning application are significant: 1) noisy
and corrupt imagery resulting in high levels of feature un-
certainty, 2) features with heavy-tailed, peaked distribu-
tions, 3) extremely imbalanced and overlapping positive
and negative data sets, and 4) the need to reach high posi-
tive classification rates, i.e. to find all potential supernovae,
while reducing the burdensome workload of manually ex-
amining false positives. High accuracy is achieved via a
sign-preserving, shifted log transform applied to features

  

Source: Aragon, Cecilia R. - Computational Research Division, Lawrence Berkeley National Laboratory
Ding, Chris - Department of Computer Science and Engineering, University of Texas at Arlington
Lawrence Berkeley National Laboratory, Computational Research Division, Distributed Systems Department, Data Intensive Distributed Computing Group
Lawrence Berkeley National Laboratory, Computational Research Division, High Performance Computing Research Department, Visualization Group
Lee, Jason R. - Data Intensive Distributed Computing Group, Distributed Systems Department, National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory

 

Collections: Computer Technologies and Information Sciences; Multidisciplinary Databases and Resources