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Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurementsстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 1 апреля 2020 г.
- Авторы: Baskakov A., Sirunyan Albert M., Belyaev A.V., Skatchkov N., Boos E., Matveev V.A., Dubinin M., Dudko L., Ershov A.A., Gribushin A., Katkov I., Klyukhin V., Kodolova O., Lokhtin I., Miagkov I., Obraztsov S., Petrushanko S., Popov A.A., Savrin V., Snigirev A., CMS Collaboration
- Журнал: European Physical Journal C
- Том: 80
- Номер: 1
- Год издания: 2020
- Издательство: Springer Verlag
- Местоположение издательства: Germany
- Первая страница: 4
- DOI: 10.1140/epjc/s10052-019-7499-4
- Аннотация: New sets of CMS underlying-event parameters ("tunes") are presented for the PYTHIA8 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 PYTHIA8 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.
- Добавил в систему: Лохтин Игорь Петрович