Multivariate Techniques for Identifying Diffractive Interactions at the LHC

• Published in: arXiv.org
• Main Authors: Kuusela, Mikael, Lamsa, Jerry W, Malmi, Eric, Mehtala, Petteri, Orava, Risto
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 16.09.2009
• Subjects: Algorithms; Classification; Computer simulation; Elastic scattering; Gene expression; Inelastic scattering; Large Hadron Collider; Multivariate analysis; Neural networks; Particle collisions; Physics - High Energy Physics - Experiment; Physics - High Energy Physics - Phenomenology; Proton scattering; Self organizing maps; Support vector machines
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.0909.3039

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.