Separating Diffractive and Non-Diffractive events in High energy Collisions at LHC energies

TL;DR

This paper presents a data-driven method using topic modeling to distinguish and extract diffractive and non-diffractive charged particle multiplicity distributions in high energy proton-proton collisions at LHC energies, aiding in precise cross-section measurements.

Contribution

The study introduces a novel application of the DEMIX statistical tool to separate diffractive from non-diffractive events in LHC collision data, demonstrating its potential for experimental analysis.

Findings

DEMIX can extract underlying distributions for diffractive and non-diffractive events.

The method is demonstrated on Pythia 8 generated data at 7 TeV.

Potential to improve measurement of inelastic cross-sections.

Abstract

The charged particle multiplicity distribution in high energy hadronic and nuclear collisions receive contribution from both diffractive and non diffractive processes. It is experimentally challenging to segregate diffractive events from non-diffractive events. The present work aims to separate and extract the charged particle multiplicity distribution of diffractive and non-diffractive events in hadronic collisions at LHC energies. A data driven model using the topic modelling statistical tool, DEMIX, has been used to demonstrate the proof of concept for p p collisions at 7 TeV generated by Pythia 8 event generator. The study suggests that DEMIX technique can be used to extract the underlying base distributions and fractions for experimental observables pertaining to diffractive and non-diffractive events at LHC energies and can therefore be used as a step forward for an experimental determination of precise inelastic cross-sections in pp collisions.