Multivariate Techniques for Identifying Diffractive Interactions at the LHC

TL;DR

This paper evaluates three multivariate analysis methods—gene expression programming, neural networks, and support vector machines—for efficiently classifying diffractive events at the LHC, aiding proton structure studies.

Contribution

It introduces and compares three multivariate techniques for identifying diffractive interactions in proton-proton collisions at the LHC.

Findings

Gene expression programming, neural networks, and support vector machines effectively classify diffractive events.

The methods enable efficient differentiation of diffractive and non-diffractive events.

Self-organizing maps visualize event characteristics for better understanding.

Abstract

Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.