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Charged particle multiplicities in pp interactions at sqrt(s) = 0.9, 2.36, and 7 TeV (1011.5531v1)

Published 24 Nov 2010 in hep-ex

Abstract: Measurements of primary charged hadron multiplicity distributions are presented for non-single-diffractive events in proton-proton collisions at centre-of-mass energies of sqrt(s) = 0.9, 2.36, and 7 TeV, in five pseudorapidity ranges from |eta|<0.5 to |eta|<2.4. The data were collected with the minimum-bias trigger of the CMS experiment during the LHC commissioning runs in 2009 and the 7 TeV run in 2010. The multiplicity distribution at sqrt(s) = 0.9 TeV is in agreement with previous measurements. At higher energies the increase of the mean multiplicity with sqrt(s) is underestimated by most event generators. The average transverse momentum as a function of the multiplicity is also presented. The measurement of higher-order moments of the multiplicity distribution confirms the violation of Koba-Nielsen-Olesen scaling that has been observed at lower energies.

Citations (160)

Summary

Analysis of Charged Particle Multiplicities in Proton-Proton Interactions at the LHC

The paper investigates charged hadron multiplicity distributions in proton-proton (pp) interactions at the Large Hadron Collider (LHC) using the Compact Muon Solenoid (CMS) detector. The analysis encompasses data from collider energies of 0.9, 2.36, and 7 TeV, focusing on non-single diffractive events. The paper employs several pseudorapidity ranges from |η| < 0.5 to |η| < 2.4 and compares these findings against established event generator models like PYTHIA and PHOJET.

Key Findings

  1. Multiplicity Distribution Analysis:
    • The multiplicity distributions at 0.9 TeV align well with prior measurements. However, at higher energies, discrepancies between the expected and observed mean multiplicities emerge, with experimental results outpacing most event generator predictions.
    • A notable feature observed is the change of slope for multiplicity distributions at high pseudorapidity ranges, indicating a multicomponent structure which intensifies at 7 TeV.
  2. Violation of KNO Scaling:
    • The measurement confirms the violation of Koba-Nielsen-Olesen (KNO) scaling for high-energy pp interactions, with significant deviations at 7 TeV. This scaling violation is more pronounced in wider pseudorapidity intervals.
    • Such deviations suggest multiple soft and semi-hard subprocesses in pp collisions and highlight the complexity of multiple-Pomeron exchanges in these interactions.
  3. Mean Multiplicity Depiction:
    • The average number of primary charged hadrons increases notably with the center-of-mass energy. This paper presents detailed comparisons with models predicting s-dependence based exponential scaling, with varying degrees of success across models.
  4. Transverse Momentum Correlation:
    • The paper presents correlations between mean transverse momentum, ⟨pT⟩, and multiplicity n. A positive correlation is consistent with theoretical expectations, suggesting models need more accurate tuning to encapsulate underlying dynamics effectively.

Model Comparison and Implications

The paper compares CMS data with outputs from Monte Carlo generators such as PYTHIADsixT and PHOJET. While PYTHIA demonstrates limitations in handling low momentum particle production, PYTHIA8 offers a nuanced model reflecting recent observations, albeit with some hyperbolic estimates of particle multiplicity when requiring pT > 500 MeV/c.

These findings highlight the need for refined phenomenological models to simulate soft and semi-hard subprocesses accurately and hint at the necessity for continuous model development to encompass rapidly emerging high-energy particle collision data.

Conclusion and Future Directions

The investigation provides a comprehensive dataset that probes deeper into the dynamics of hadronic collisions at unprecedented energies, revealing inconsistencies in existing theoretical predictions. The pronounced KNO scaling violations and the intricate mean multiplicity trends call for revisiting theoretical frameworks governing soft QCD processes. Future developments in AI-driven networks might play a crucial role in analyzing complex high-volume data with higher precision, potentially leading to breakthroughs in hadron collision dynamics and particle production modeling.

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