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In this contribution the new event generation framework SHERPA will be presented, which aims at a full simulation of events at current and future high-energy experiments. Some first results exemplify its capabilities.
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We present numerical tests and predictions of the KrkNLO method for matching of NLO QCD corrections to hard processes with LO parton shower Monte Carlo generators (NLO+PS). This method was described in detail in our previous publications, where it was also compared with other NLO+PS matching approaches (MC@NLO and POWHEG) as well as fixed-order NLO and NNLO calculations. Here we concentrate on presenting some numerical results (cross sections and distributions) for $Z/gamma^*$ (Drell-Yan) and Higgs-boson production processes at the LHC. The Drell--Yan process is used mainly to validate the KrkNLO implementation in the Herwig 7 program with respect to the previous implementation in Sherpa. We also show predictions for this process with the new, complete, MC-scheme parton distribution functions and compare them with our previously published results. Then, we present the first results of the KrkNLO method for Higgs production in gluon-gluon fusion at the LHC and compare them with MC@NLO and POWHEG predictions from Herwig 7 fixed-order results from HNNLO and a resummed calculation from HqT, as well as with experimental data from the ATLAS collaboration.
Next-to-leading order (NLO) QCD predictions for the production of heavy quarks in proton-proton collisions are presented within three different approaches to quark mass, resummation and fragmentation effects. In particular, new NLO and parton shower simulations with POWHEG are performed in the ALICE kinematic regime at three different centre-of-mass energies, including scale and parton density variations, in order to establish a reliable baseline for future detailed studies of heavy-quark suppression in heavy-ion collisions. Very good agreement of POWHEG is found with FONLL, in particular for centrally produced D^0, D^+ and D^*+ mesons and electrons from charm and bottom quark decays, but also with the generally somewhat higher GM-VFNS predictions within the theoretical uncertainties. The latter are dominated by scale rather than quark mass variations. Parton density uncertainties for charm and bottom quark production are computed here with POWHEG for the first time and shown to be dominant in the forward regime, e.g. for muons coming from heavy-flavour decays. The fragmentation into D_s^+ mesons seems to require further tuning within the NLO Monte Carlo approach.
We review the main software and computing challenges for the Monte Carlo physics event generators used by the LHC experiments, in view of the High-Luminosity LHC (HL-LHC) physics programme. This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group as an input to the LHCC review of HL-LHC computing, which has started in May 2020.
Models of neutrino mass generation provide well motivated scenarios of Beyond-the-Standard-Model physics. The synergy between low energy and high energy LHC searches facilitates an effective approach to rule out, constrain or ideally pinpoint such models. In this proceedings report, we provide a brief overview of scenarios where searches at the LHC can help determine the mechanism of light neutrino masses and potentially falsify baryogenesis mechanisms.
While the Quasi-Monte Carlo method of numerical integration achieves smaller integration error than standard Monte Carlo, its use in particle physics phenomenology has been hindered by the abscence of a reliable way to estimate that error. The standard Monte Carlo error estimator relies on the assumption that the points are generated independently of each other and, therefore, fails to account for the error improvement advertised by the Quasi-Monte Carlo method. We advocate the construction of an estimator of stochastic nature, based on the ensemble of pointsets with a particular discrepancy value. We investigate the consequences of this choice and give some first empirical results on the suggested estimators.
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