Soft Classification of Diffractive Interactions at the LHC

TL;DR

This paper introduces a soft classification method using posterior probabilities in machine learning to improve the identification of diffractive events at the LHC, leading to more accurate physical observable estimations.

Contribution

It proposes a novel soft classification approach that estimates posterior probabilities, enhancing the accuracy of diffractive event classification over traditional hard methods.

Findings

Soft classification better reproduces multiplicity distributions.

It improves the accuracy of relative event rate estimations.

The method outperforms traditional hard classification in Monte Carlo simulations.

Abstract

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.