Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression
This provides a novel method for high-energy physics researchers to derive fragmentation functions from data without pre-assumed forms, though it is incremental as it applies an existing ML technique to a specific domain problem.
The study tackled the problem of determining fragmentation functions, which are key for describing hadron production in high-energy physics but cannot be calculated theoretically, by using symbolic regression to infer a functional form directly from experimental data. The learned function resembles the Lund string function and describes the data well, offering a potential candidate for global fits.
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.