Study of hadronic event-shape variables in multijet final states in pp collisions at sqrt(s) = 7 TeV

TL;DR

This CMS study evaluates five transverse hadronic event-shape variables in multijet pp collisions at $\sqrt{s}=7$ TeV using up to $5\,\text{fb}^{-1}$, comparing unfolded data to multiple QCD MC generators. The analysis defines $\tau_{\perp}$, $B_{\text{tot}}$, $\rho_{\text{tot}}$, $\rho^{\mathrm{T}}_{\text{tot}}$, and $Y_{23}$ in the transverse plane and unfolds detector effects with a Bayesian method using MC-derived response matrices. Among generators, MadGraph combined with Pythia6-$\text{Z2}$ (PS/UE) best reproduces all observables within uncertainties, while others show varying levels of disagreement, especially for $B_{\text{tot}}$ and $\rho_{\text{tot}}$, which are more sensitive to MPI and colour connections. The results provide detailed constraints to improve parton radiation and hadronization modelling in high-energy hadronic collisions, informing future MC tuning and QCD studies.

Abstract

Event-shape variables, which are sensitive to perturbative and nonperturbative aspects of quantum chromodynamic (QCD) interactions, are studied in multijet events recorded in proton-proton collisions at sqrt(s) = 7 TeV. Events are selected with at least one jet with transverse momentum pt > 110 GeV and pseudorapidity abs(eta) < 2.4, in a data sample corresponding to integrated luminosities of up to 5 inverse femtobarns. The distributions of five event-shape variables in various leading jet pt ranges are compared to predictions from different QCD Monte Carlo event generators.

Figures (5)

• Figure 1: (a,d,g,j,m) Comparison between the transverse thrust $\tau_{\perp}\xspace$ distributions in data and MadGraph+pythia6-Z2 event generator in five different ranges of $p_{\mathrm {T,1}}$. The error bars around the data points indicate the statistical uncertainties in data. The panels (b,e,h,k,n) show the ratios of different models of the pythia6 event generator over data in each momentum range and panels (c,f,i,l,o) show the ratios for other generators. The shaded bands represent statistical and systematic uncertainties in data.
• Figure 2: Comparison between the jet broadening $B_\text{tot}\xspace$ distributions in data and various Monte Carlo models. The $p_{\mathrm{T}}$ bins and other details are the same as in Fig. \ref{['fig:finalhlt_thrust']}.
• Figure 3: Comparison between the total jet mass $\rho_{\text{tot}}\xspace$ distributions in data and various Monte Carlo models. The $p_{\mathrm{T}}$ bins and other details are the same as in Fig. \ref{['fig:finalhlt_thrust']}.
• Figure 4: Comparison between the total jet transverse mass $\rho^\mathrm{T}_\text{tot}\xspace$ distributions in data and various Monte Carlo models. The $p_{\mathrm{T}}$ bins and other details are the same as in Fig. \ref{['fig:finalhlt_thrust']}.
• Figure 5: Comparison between the third-jet resolution parameter $Y_{23}\xspace$ in data and various Monte Carlo models. The $p_{\mathrm{T}}$ bins and other details are the same as in Fig. \ref{['fig:finalhlt_thrust']}.