Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression

TL;DR

This paper introduces a novel machine learning approach using symbolic regression to directly infer interpretable analytical models of fragmentation functions from experimental data in high-energy physics, bypassing traditional fitting methods.

Contribution

It is the first to apply symbolic regression to derive functional forms of fragmentation functions directly from experimental measurements.

Findings

Learned function resembles Lund string function

Model describes data well

Potential for use in global FF fits

Abstract

Machine learning is rapidly making its path into natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. The latter represent a key ingredient to describe physical observables measured in high-energy physics processes that involve hadron production, and predict their values at different energy. Fragmentation functions can not be calculated in theory and have to be determined instead from data. Traditional approaches rely on global fits of experimental data using a pre-assumed functional form inspired from phenomenological models to learn its parameters. This novel approach uses a ML technique, namely symbolic regression, to learn an analytical model from measured charged hadron multiplicities. The function learned by symbolic regression resembles the Lund string function and describes the data well, thus representing a potential candidate for use in global FFs fits. This study represents an approach to follow in such QCD-related phenomenology studies and more generally in sciences.