Soft Classification of Diffractive Interactions at the LHC

Details Soft Classification of Diffractive Interactions at the LHC Soft Classification of Diffractive Interactions at the LHC

2010-12-30

arxiv.org/abs/1101.0090v1

Physics

High Energy Physics

High Energy Physics - Experiment

4 pages, 1 figure, talk given by M. Kuusela at Diffraction 2010, Otranto, Italy (September 2010)

Scientific paper

Multivariate machine learning techniques provide an alternative to the rapidity gap method for event-by-event identification and classification of diffraction in hadron-hadron collisions. Traditionally, such methods assign each event exclusively to a single class producing classification errors in overlap regions of data space. As an alternative to this so called hard classification approach, we propose estimating posterior probabilities of each diffractive class and using these estimates to weigh event contributions to physical observables. It is shown with a Monte Carlo study that such a soft classification scheme is able to reproduce observables such as multiplicity distributions and relative event rates with a much higher accuracy than hard classification.

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