Application of Genetic Programming to High Energy Physics Event Selection

TL;DR

This paper demonstrates that genetic programming can effectively improve event selection in high energy physics, specifically for studying decay processes, marking the first such application in this field.

Contribution

It introduces the novel application of genetic programming to high energy physics data analysis, showing improved results over traditional methods.

Findings

Genetic programming outperforms traditional analysis techniques.

First application of genetic programming in high energy physics.

Effective in studying specific particle decay processes.

Abstract

We review genetic programming principles, their application to FOCUS data samples, and use the method to study the doubly Cabibbo suppressed decay D+ -> K+ pi+ pi- relative to its Cabibbo favored counterpart, D+ -> K- pi+ pi+. We find that this technique is able to improve upon more traditional analysis methods. To our knowledge, this is the first application of the genetic programming technique to High Energy Physics data.