Jet and underlying event properties as a function of charged-particle multiplicity in proton-proton collisions at √s = 7 TeV

Chatrchyan, S. and Khachatryan, V. and Sirunyan, A. M. and Tumasyan, A. and Adam, W. and Bergauer, T. and Dragicevic, M. and Erö, J. and Fabjan, C. and Friedl, M. and Frühwirth, R. and Ghete, V. M. and Hörmann, N. and Hrubec, J. and Jeitler, M. and Kiesenhofer, W. and Knünz, V. and Krammer, M. and Krätschmer, I. and Liko, D. and Mikulec, I. and Rabady, D. and Rahbaran, B. and Rohringer, C. and Rohringer, H. and Schöfbeck, R. and Strauss, J. and Taurok, A. and Treberer-Treberspurg, W. and Waltenberger, W. and Bencze, György and Hajdu, Csaba and Hidas, Pál and Horváth, Dezső and Siklér, Ferenc and Veszprémi, Viktor and Vesztergombi, György and Zsigmond, Anna Júlia and Molnár, József and Pálinkás, József and Karancsi, János and Trócsányi, Zoltán and Ujvári, Balázs and Krajczár, Krisztián and Veres, Gábor (2013) Jet and underlying event properties as a function of charged-particle multiplicity in proton-proton collisions at √s = 7 TeV. EUROPEAN PHYSICAL JOURNAL C, 73 (12). ISSN 1434-6044

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Abstract

Characteristics of multi-particle production in proton-proton collisions at √ s = 7 TeV are studied as a function of the charged-particle multiplicity, Nch. 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 pT > 0.25 GeV/c. Jets are reconstructed from chargedparticles only and required to have pT > 5 GeV/c. The distributions of jet pT, average pT of charged particles belonging to the underlying event or to jets, jet rates, and jet shapes are presented as functions of Nch and compared to the predictions of the PYTHIA and HERWIG event generators. Predictions without multi-parton interactions fail completely to describe the Nch-dependence observed in the data. For increasing Nch, PYTHIA systematically predicts higher jet rates and harder pT 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.

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