Merging multi-leg NLO matrix elements with parton showers View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-03

AUTHORS

Leif Lönnblad, Stefan Prestel

ABSTRACT

We discuss extensions of multi-jet matrix element and parton shower merging approaches, to also include next-to-leading order accuracy. Specifically, we generalise the so-called CKKW-L prescription and the recently developed unitarised matrix element + parton shower (UMEPS) scheme. Endowing tree-level merging methods with NLO corrections greatly enhances the perturbative accuracy of parton shower Monte Carlo programs. To generalise the CKKW-L approach, we augment the Nils-Lavesson-Leif-Lönnblad (NL3) scheme, which was previously developed for e+e−-annihilation, with a careful treatment of parton densities. This makes the application of the NL3 method to hadronic collisions possible. NL3 is further updated to use for more readily accessible next-to-leading order input calculations. We also extend the UMEPS scheme to NLO accuracy. The resulting approach, dubbed unitarised next-to-leading order + parton shower (UNLOPS) merging, does not inherit problematic unitarity-breaking features of CKKW-L, and thus allows for a theoretically more appealing definition of NLO order merging. Both schemes have been implemented in PYTHIA8. We present results for the merging of W- and Higgs-production events, where the zero- and one-jet contribution are corrected to next-to-leading order simultaneously, and higher jet multiplicities are described by tree-level matrix elements. We find that NL3 and UNLOPS yield a very similar description for W production. For Higgs production however, UNLOPS produces more stable results. The implementation of the NLO merging procedures is completely general and can be used for higher jet multiplicities and other processes, subject to the availability of programs able to correctly generate the corresponding partonic states to leading and next-to-leading order accuracy. More... »

PAGES

166

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  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/jhep03(2013)166

    DOI

    http://dx.doi.org/10.1007/jhep03(2013)166

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010079168


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