Application of Gene Expression Programming in Improving the Event Selection of the Semi-leptonic Top Quark Pair Process

TL;DR

This paper demonstrates how Gene Expression Programming can optimize event selection classifiers in high-energy physics, specifically for top-quark pair production, improving data purity and measurement flexibility.

Contribution

It introduces a GEP-based selection algorithm tailored for top-quark pair events, enhancing purity and allowing adjustable trade-offs for specific measurements.

Findings

GEP achieves high classifier accuracy in top-quark event selection.

Adding penalty cuts enables tailored optimization for data purity and sample size.

The method improves event selection precision in semi-leptonic decay channels.

Abstract

Searches for Beyond the Standard Model physics require probing the Standard Model with increased precision. One way this can be achieved is by improving the accuracy of the event selection classifiers. Recently, Gene Expression Programming (GEP) has been shown to provide complex yet easy to interpret classifiers in various fields. Previous attempts to apply GEP to high-energy physics (HEP), though limited by computational power available, achieved classifier accuracy of up to 95\%. In this paper, we demonstrate that a selection algorithm optimized by GEP and applied to the top-quark pair production process' semi-leptonic decay channel enables the increase of data purity for already highly pure data. Moreover, we explain how adding penalty cuts to the purity fitness function allows adjusting the optimized classifier to the needs of a specific measurement in terms of the size of the selected event sample and data purity.