Speakers
Dr
Ilya Narsky
(California Institute of Technology)Mr
Julian Bunn
(CALTECH)Dr
Julian Bunn
(CALTECH)
Julian Bunn
(California Institute of Technology (CALTECH))
Description
Modern analysis of high energy physics (HEP) data needs advanced statistical tools to
separate signal from background. A C++ package has been implemented to provide such
tools for the HEP community. The package includes linear and quadratic discriminant
analysis, decision trees, bump hunting (PRIM), boosting (AdaBoost), bagging and
random forest algorithms, and interfaces to the feedforward backpropagation neural
net and radial basis function neural net implemented in the Stuttgart Neural Network
Simulator. Supplemental tools such as random number generators, bootstrap, estimation
of data moments, and a test of zero correlation between two variables with a joint
elliptical distribution are also provided. Input data can be read from ascii and Root
files. The package offers a convenient set of tools for imposing requirements on
input data and displaying output. Integrated in the BaBar computing environment, the
package maintains a minimal set of BaBar dependencies and can be easily adapted to
any other HEP environment. It has been tested at BaBar on several physics-analysis
datasets.
Primary author
Dr
Ilya Narsky
(California Institute of Technology)
Co-author
Akram Khan
(Brunel University)
