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

TL;DR

StatPatternRecognition is a comprehensive C++ package offering advanced statistical tools like discriminant analysis, decision trees, boosting, and neural networks, tailored for high energy physics data analysis with easy integration and minimal dependencies.

Contribution

It introduces a versatile C++ package with multiple statistical analysis tools specifically designed for high energy physics data, enhancing analysis capabilities and ease of use.

Findings

Successfully tested on various idealistic and realistic examples.

Provides a broad set of statistical tools in a unified package.

Easily adaptable to different computing environments.

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.