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Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements
Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements
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Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements
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Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements
Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements

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Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements
Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements
Journal Article

Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements

2020
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Overview
New sets of CMS underlying-event parameters (“tunes”) are presented for the pythia 8 event generator. These tunes use the NNPDF3.1 parton distribution functions (PDFs) at leading (LO), next-to-leading (NLO), or next-to-next-to-leading (NNLO) orders in perturbative quantum chromodynamics, and the strong coupling evolution at LO or NLO. Measurements of charged-particle multiplicity and transverse momentum densities at various hadron collision energies are fit simultaneously to determine the parameters of the tunes. Comparisons of the predictions of the new tunes are provided for observables sensitive to the event shapes at LEP, global underlying event, soft multiparton interactions, and double-parton scattering contributions. In addition, comparisons are made for observables measured in various specific processes, such as multijet, Drell–Yan, and top quark-antiquark pair production including jet substructure observables. The simulation of the underlying event provided by the new tunes is interfaced to a higher-order matrix-element calculation. For the first time, predictions from pythia 8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.