  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.
