StatPatternRecognition: A C++ Package for Statistical Analysis of High Energy Physics Data

Abstract

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 standard backpropagation neural net and radial basis function neural net implemented in the Stuttgart Neural Network Simulator. Supplemental tools such as bootstrap, estimation of data moments, and a test of zero correlation between two variables with a joint elliptical distribution are also provided. 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 external dependencies and therefore can be easily adapted to any other environment. It has been tested on many idealistic and realistic examples.

31 pages, 11 figures, 2 tables

References in corpus (1)

• Estimation of Goodness-of-Fit in Multidimensional Analysis Using Distance to Nearest Neighbor

Cited by in corpus (7)

• First observations of ${\bar{B}}_s^0\to D^{+}{D}^{-}$, $D_s^{+}{D}^{-}$ and $D^0{\bar{D}}^0$ decays
• Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees
• Exclusive b \to s (d) ll Decays
• Comparison of the CPU and memory performance of StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA)
• A multivariate approach to heavy flavour tagging with cascade training
• Formalism for Simulation-based Optimization of Measurement Errors in High Energy Physics
• Diboson Production Cross Sections at $\sqrt{s}=1.96$ TeV