Characteristics of multi-particle production in proton-proton collisions at $\sqrt{s}$=7 TeV are studied as a function of the charged-particle multiplicity, $N_{ch}$. The produced particles are separated into two classes: those belonging to jets and those belonging to the underlying event. Charged particles are measured with pseudorapidity |η|<2.4 and transverse momentum $p_T$ > 0.25 GeV/c. Jets are reconstructed from charged-particles only and required to have $p_T$ > 5 GeV/c. The distributions of jet $p_T$, average $p_T$ of charged particles belonging to the underlying event or to jets, jet rates, and jet shapes are presented as functions of $N_{ch}$ and compared to the predictions of the PYTHIA and HERWIG event generators. Predictions without multi-parton interactions fail completely to describe the $N_{ch}$-dependence observed in the data. For increasing $N_{ch}$, PYTHIA systematically predicts higher jet rates and harder $p_T$ spectra than seen in the data, whereas HERWIG shows the opposite trends. At the highest multiplicity, the data–model agreement is worse for most observables, indicating the need for further tuning and/or new model ingredients.
Jet shapes have been measured in inclusive jet production in proton-proton collisions at sqrt(s) = 7 TeV using 3 pb^{-1} of data recorded by the ATLAS experiment at the LHC. Jets are reconstructed using the anti-kt algorithm with transverse momentum 30 GeV < pT < 600 GeV and rapidity in the region |y| < 2.8. The data are corrected for detector effects and compared to several leading-order QCD matrix elements plus parton shower Monte Carlo predictions, including different sets of parameters tuned to model fragmentation processes and underlying event contributions in the final state. The measured jets become narrower with increasing jet transverse momentum and the jet shapes present a moderate jet rapidity dependence. Within QCD, the data test a variety of perturbative and non-perturbative effects. In particular, the data show sensitivity to the details of the parton shower, fragmentation, and underlying event models in the Monte Carlo generators. For an appropriate choice of the parameters used in these models, the data are well described.
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