New approaches for boosting to uniformity

TL;DR

This paper introduces new boosting algorithms aimed at achieving uniform selection efficiency in multivariate spaces, enhancing particle physics analyses by reducing bias and false signals.

Contribution

The paper presents novel boosting methods specifically designed to produce uniform efficiency across multivariate spaces in particle physics applications.

Findings

Algorithms successfully produce uniform efficiency across Dalitz-plots

Methods prevent false signal peaks in invariant mass distributions

Enhanced robustness in particle detection and analysis

Abstract

The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting. This paper explores several novel boosting methods that have been designed to produce a uniform selection efficiency in a chosen multivariate space. Such algorithms have a wide range of applications in particle physics, from producing uniform signal selection efficiency across a Dalitz-plot to avoiding the creation of false signal peaks in an invariant mass distribution when searching for new particles.