Measurement of energy flow at large pseudorapidities in pp collisions at sqrt(s) = 0.9 and 7 TeV

TL;DR

The study measures forward energy flow $dE/d\eta$ in pp collisions at $\sqrt{s}=0.9$ and 7 TeV using CMS HF calorimeters, in the forward region $3.15<|\eta|<4.9$, for minimum-bias and central dijet events. By comparing to a wide suite of Monte Carlo models, the analysis shows that including multiple-parton interactions sharply improves agreement with data, while models without MPI underpredict the forward energy. MPI-sensitive tunes yield varying degrees of success across MB and DJ samples, highlighting substantial model dependence and the value of forward observables in constraining UE MPI parameters. The transverse energy flow and the energy-flow shape reveal a stronger rise with energy in forward regions than in DIS, illustrating the significant role of MPI at LHC energies and guiding future tuning of hadronic interaction models.

Abstract

The energy flow, dE/d(eta), is studied at large pseudorapidities in proton-proton collisions at the LHC, for centre-of-mass energies of 0.9 and 7 TeV. The measurements are made in the pseudorapidity range 3.15 < |eta| < 4.9, for both minimum-bias events and events with at least two high-momentum jets, using the CMS detector. The data are compared to various pp Monte Carlo event generators whose theoretical models and input parameter values are sensitive to the energy-flow measurements. Inclusion of multiple-parton interactions in the Monte Carlo event generators is found to improve the description of the energy-flow measurements.

Figures (4)

• Figure 1: Energy flow as a function of $\eta$ for minimum-bias (upper) and dijet (lower) events at $\sqrt{s} = 0.9$ and 7 TeV. The data are shown as points with error bars, while the histograms correspond to predictions obtained from various pythia6 tunes. The error bars represent the systematic uncertainties, which are strongly correlated between the bins. The statistical uncertainties are negligible. The lower panels show the ratio of MC prediction to data.
• Figure 2: Energy flow as a function of $\eta$ for minimum-bias (upper) and dijet (lower) events at $\sqrt{s} = 0.9$ TeV and $\sqrt{s} = 7$ TeV. The data are shown as points with error bars, while the histograms correspond to predictions obtained from various Monte Carlo event generators. The yellow bands illustrate the spread of the predictions from the different pythia6 tunes considered. The bands are obtained by taking the minimum and maximum variations of the pythia6 tunes shown in fig. \ref{['fig:results_mb_mctunes']}. The predictions from herwig++ are made with tunes specific to the respective centre-of-mass energy. The error bars represent the systematic uncertainties, which are strongly correlated between the bins. The statistical uncertainties are negligible. The lower panels show the ratio of MC prediction to data.
• Figure 3: Energy flow as a function of $\eta$ for minimum-bias (upper) and dijet (lower) events at $\sqrt{s} = 0.9$ TeV and $\sqrt{s} = 7$ TeV. The data are shown as points with error bars, while the histograms correspond to predictions obtained from different cosmic-ray Monte Carlo event generators. The error bars represent the systematic uncertainties, which are strongly correlated between the bins. The statistical uncertainties are negligible. The lower panels show the ratio of MC prediction to data.
• Figure 4: Transverse energy flow as a function of $\eta$ for minimum-bias and dijet events at $\sqrt{s} = 0.9$ TeV and $\sqrt{s} = 7$ TeV. The data are shown as points with error bars. The error bars represent the systematic uncertainties, which are strongly correlated between the bins. The statistical uncertainties are negligible.