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.
A measurement of the underlying activity in scattering processes with a hard scale in the several GeV region is performed in proton-proton collisions at sqrt(s) = 0.9 and 7 TeV, using data collected by the CMS experiment at the LHC. The production of charged particles with pseudorapidity |eta| < 2 and transverse momentum pT > 0.5 GeV/c is studied in the azimuthal region transverse to that of the leading set of charged particles forming a track-jet. A significant growth of the average multiplicity and scalar-pT sum of the particles in the transverse region is observed with increasing pT of the leading track-jet, followed by a much slower rise above a few GeV/c. For track-jet pT larger than a few GeV/c, the activity in the transverse region is approximately doubled with a centre-of-mass energy increase from 0.9 to 7 TeV. Predictions of several QCD-inspired models as implemented in PYTHIA are compared to the data.
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