Event generation with

By T. Gleisberg, S. Höche, F. Krauss, Steffen Schumann, F. Siegert, J. Winter. 2009

217 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/98862131a08639c70a7b84ff2db7d76499824c70

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1. Cms Collaboration
2. A. Larkoski
3. Atlas Collaboration
4. S. Höche
5. I. Moult
6. S. Marzani
7. R. Frederix
8. G. Salam

Citations in the corpus

• On-the-fly reduction of open loops (2017, unknown): “ 2 D N 1 q = = Z dDq¯ Tr h N¯(I N,q,h¯ ) i D¯ 0 D¯ 1 ···D¯ N−1 . (20) Symbols carrying a bar denote quantities in D= 4−2ε dimensions, and for the loop momentum ¯qwe adopt the decomposition ¯q= q+ ˜q, (21) where qand ˜qdenote its four-dimensional and (D−4)- dimensional parts, respectively. The denomi”
• Event generation with Sherpa 2.2 (2019, methodology): “ispensable tools for the design, realisation, analysis and interpretation of high-energy scattering experiments. In particular, general-purpose generators such as P YTHIA [1], H ERWIG [2] and S HERPA [3] are necessary to address detailed aspects of the nal states produced in individual scattering ev”
• Systematics of quark/gluon tagging (2017, methodology): “pare seven dierent parton-shower generators both before hadronization (\parton level") and after hadronization (\hadron level"): Pythia 8.215 [39], Herwig++ 2.7.1 [40,41],2 Sherpa 2.2.1 [45], Vincia 2.001 [46], Deductor 1.0.2 [47] (with hadronization performed by Pythia 8.212),3 1Of course”
• Vectorlike confinement at the LHC (2009, methodology): “events, which interpolates between the 2 → 1 approximation for heavy axions and the 2 → 2 approximation for light axions. To accomplish this, we have generated a matched event sample using SHERPA 1.1 [45] with ISR turned on. Implementing the experimental cuts used in [41], we find that the existence ”
• The automation of next-to-leading order electroweak calculations (2018, result): “e as for QCD computations, there has been steady progress in the automation of EW NLO corrections, for both one-loop and realemission contributions, by collaborations such as RECOLA [8,9] with Sherpa [10,11], OpenLoops [12] with Sherpa ,GoSam 13 14] with either MadDipole [15,16] or Sherpa and MadGra”
• Reweighting QCD matrix-element and parton-shower calculations (2016, background): “ical predictions with actual collider data, Monte-Carlo event generators prove to be an indispensable tool. In particular parton-shower Monte-Carlo programmes like Herwig [4,5], Pythia [6] and Sherpa [7,8] provide simulations at the level of exclusive particle-level nal states [9]. The cornerstones ”
• The Angantyr model for heavy-ion collisions in Pythia8 (2018, background): “ collisions and heavy ion collisions respectively, are often carried out in quite different ways. In the case of pp collisions, so–called ”general purpose Monte Carlo event generators”, such as SHERPA [1], Herwig 7 [2] and PYTHIA8 [3], have been established as cornerstones in aiding our understanding”
• Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 (2013, methodology): “t;vp T . Boosted objects at the LHC 5 can be extended to 2(n k) m2nby also including the order k s contribution to g () 0 . Typical Monte Carlo event generators such as Pythia, Herwig++ and Sherpa [23] are correct at LL. NLL accuracy has also been achieved for some specic observables, but it is dicu”
• Building and testing models with extended Higgs sectors (2017, background): “lculators implement specic bSM models, generate vertices, and transfer them to event generators. Versatile general-purpose matrix element generators such as MadGraph [134], CalcHEP [135], and Sherpa [136] construct and evaluate Feynman diagrams corresponding to a desired process, and then feed the r”
• Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “exist, each of which has its own advantages and disadvantages, and emphasizes dierent physics aspects. Examples include Pythia [142,143,144], a pT-ordered dipole shower; Vincia [145,146,147], Sherpa [148,149], Ariadne [150,151], and DIRE [152], dipole-antenna showers; Herwig++/Herwig7 [153,154,155,1”
• Scattering amplitudes from unitarity-based reduction algorithm at the integrand-level (2010, background): “o-leading order corrections becomes mandatory. Leading order (LO) processes are the core of well-established multi-purpose event generators like MadGraph MadEvent [1–3], CompHEP-CalcHEP [4,5], SHERPA [6,7], WHIZARD [8], ALPGEN [9], and HELAC [10,11], whereas a variety of processes computed at NLO ar”