[Paper Review] Soft QCD Measurements at LHCb

P. Ilten|arXiv (Cornell University)|Sep 1, 2014

Particle physics theoretical and experimental studies17 references3 citations

TL;DR

This paper presents forward-region soft QCD measurements from LHCb using 7 TeV proton-proton collisions, focusing on charged particle multiplicity and energy flow to test underlying event and hadronization models. It evaluates Monte Carlo generators like Pythia and Herwig++ with LHC data, finding that Pythia 8 tune-4C and LHCb-tuned models best describe the data, especially in high-pseudorapidity regions.

ABSTRACT

Studies in the forward region of charged particle multiplicity and density, as well as energy flow, are presented. These measurements are performed using data from proton-proton collisions at a center-of-mass energy of 7 TeV, collected with the LHCb detector. The results are compared to predictions from a variety of Monte Carlo event generators and are used to test underlying event and hadronization models as well as the performance of event generator tunes in the forward region.

Motivation & Objective

• To provide new experimental data on soft QCD processes in the forward pseudorapidity region (2 ≤ η ≤ 5) using the LHCb detector.
• To test the performance of Monte Carlo event generators in modeling underlying event and hadronization, particularly in the forward region.
• To disentangle the effects of initial-state radiation, final-state radiation, and multi-parton interactions (MPI) on particle multiplicity and energy flow.
• To validate and tune hadronization models (string vs. cluster) and MPI models using infrared-sensitive and infrared-safe observables.
• To supply Rivet-compatible analysis plugins for broader use in tuning event generators across the high-energy physics community.

Proposed method

• Measure charged particle multiplicity and density in pseudorapidity bins (2.0 < η < 4.8) with transverse momentum > 0.2 GeV and momentum > 2 GeV.
• Apply event-by-event reweighting to correct for reconstruction inefficiencies, track purity (6.5% artifacts, 1% duplicates, 4.5% non-prompt), and no-track acceptance.
• Unfold pile-up effects and detector resolution using bin-to-bin correction matrices to recover single-interaction distributions.
• Define energy flow as the average charged particle energy per unit pseudorapidity, using tracks with 2 < p < 1000 GeV.
• Subdivide events into hard-scattering, non-diffractive, and diffractive enriched samples based on track multiplicity and pseudorapidity.
• Compare data to predictions from Pythia 6/8, Herwig++, and air-shower generators (Epos, QGSJet, Sibyll), using systematic uncertainty estimation from varied Pythia configurations.

Experimental results

Research questions

• RQ1 How well do Pythia and Herwig++ event generators describe charged particle multiplicity and density in the forward region (2 < η < 4.8) at √s = 7 TeV?
• RQ2 Which hadronization model—string or cluster—better describes the observed particle multiplicity and momentum distributions in LHCb's forward geometry?
• RQ3 How do different tunes of Pythia 8 and Herwig++ perform in modeling the underlying event and multi-parton interactions in the forward region?
• RQ4 To what extent do hard-to-soft and soft-to-hard MPI models accurately describe the measured charged energy flow in different event types?
• RQ5 Do non-LHC-tuned event generator configurations systematically deviate from data in the forward region, and can LHCb data improve tuning?

Key findings

• Pythia 8 tune-4C and the LHCb-tuned Pythia 8 configuration provide the best overall agreement with measured charged particle multiplicity and density across pseudorapidity and transverse momentum bins.
• The LHCb-tuned Pythia 8 and Herwig++ UE-EE-5-MRST tunes describe the data well, while non-LHC-tuned models systematically underestimate particle density in the forward region.
• For energy flow, Pythia 8 consistently overestimates hard-scattering events at large η, while Pythia 6 underestimates at high η; both are improved with color reconnection tuning.
• The non-diffractive sample is well described by Pythia 8, Pythia 6 without color reconnection, Epos, and Sibyll, while QgsJet generators overestimate across all η regions.
• Diffractive-enriched events are well modeled by Pythia 8 but underestimated by all other generators, indicating a need for improved diffractive MPI modeling.
• The total energy flow, estimated from charged energy flow and neutral-to-charged ratios, shows similar trends to charged energy flow, with consistent model deviations at high pseudorapidity.