Speaker
Description
Physics event generators are essential components of the simulation software chain of HEP experiments, providing theoretical predictions against which experimental data are compared. In the LHC experiments, the simulation of QCD physics processes at the Next-to-Leading-Order (NLO) or beyond is essential to reach the level of accuracy required. However, a distinctive feature of QCD NLO generators such as Madgraph5_aMC@NLO (MG5aMC) is that some events are generated with negative weights: this is a problem because the number of MC events that must be generated and processed rapidly increases with the fraction of negative weights. In this presentation, we report on new techniques which we are developing to reduce the fraction of negative-weight NLO events in MG5aMC, notably by using Machine Learning approaches. After presenting the method, we discuss some results based on toy models and proof-of-concept tests in MG5aMC, as well as the prospects for implementing this new approach for production use by the experiments.