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A Monte-Carlo simulation of double parton scattering
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Abstract
In this work, a new Monte-Carlo simulation of double parton scattering (DPS) at parton level is presented. The simulation is based on the QCD framework developed recently by M. Diehl, J. R. Gaunt and K. Schönwald. With this framework, the dynamics of the 1 → 2 perturbative splittings is consistently included inside the simulation, with the impact-parameter dependence taken into account. The simulation evolves simultaneously two hard systems from a common hard scale down to the hadronic scale. The evolution is performed using an angular-ordered parton shower which is combined with a set of double parton distributions that depend explicitly on the inter-parton distance. An illustrative study is performed in the context of same-sign WW production at the LHC, with the quark content of the proton being limited to three flavours. In several distributions we see differences compared to DPS models in Herwig, Pythia, and the DPS “pocket formula”.
Springer Science and Business Media LLC
Title: A Monte-Carlo simulation of double parton scattering
Description:
Abstract
In this work, a new Monte-Carlo simulation of double parton scattering (DPS) at parton level is presented.
The simulation is based on the QCD framework developed recently by M.
Diehl, J.
R.
Gaunt and K.
Schönwald.
With this framework, the dynamics of the 1 → 2 perturbative splittings is consistently included inside the simulation, with the impact-parameter dependence taken into account.
The simulation evolves simultaneously two hard systems from a common hard scale down to the hadronic scale.
The evolution is performed using an angular-ordered parton shower which is combined with a set of double parton distributions that depend explicitly on the inter-parton distance.
An illustrative study is performed in the context of same-sign WW production at the LHC, with the quark content of the proton being limited to three flavours.
In several distributions we see differences compared to DPS models in Herwig, Pythia, and the DPS “pocket formula”.
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