Optimization of Signal Significance by Bagging Decision Trees

TL;DR

This paper presents a bagging decision tree algorithm optimized for maximizing signal significance in high energy physics data analysis, demonstrating superior performance over existing classifiers.

Contribution

The paper introduces a novel bagging decision tree method tailored for optimizing classification figures of merit like signal significance in HEP data.

Findings

Increased expected signal significance from 2.4 sigma to 3.0 sigma in B->gamma e nu analysis.

Outperforms boosted decision trees and other classifiers in HEP data classification.

Effective in real-world physics analysis at BaBar experiment.

Abstract

An algorithm for optimization of signal significance or any other classification figure of merit suited for analysis of high energy physics (HEP) data is described. This algorithm trains decision trees on many bootstrap replicas of training data with each tree required to optimize the signal significance or any other chosen figure of merit. New data are then classified by a simple majority vote of the built trees. The performance of this algorithm has been studied using a search for the radiative leptonic decay B->gamma l nu at BaBar and shown to be superior to that of all other attempted classifiers including such powerful methods as boosted decision trees. In the B->gamma e nu channel, the described algorithm increases the expected signal significance from 2.4 sigma obtained by an original method designed for the B->gamma l nu analysis to 3.0 sigma.