Speaker
Description
Negative weights in next-to-leading-order (NLO) event generation pose a significant computational challenge in collider physics. In this talk, I will describe a new Monte Carlo technique called ARCANE reweighting for tackling the negative weights problem. By applying ARCANE reweighting, one can reduce or even completely eliminate the negative weights in Monte Carlo datasets a) without introducing any biases in the distribution of physical observables, b) without requiring any changes to the matching and merging prescriptions used in NLO event generation, and c) without introducing any uncertainties that will not be captured by the standard error formulas used in HEP data analyses.
I will demonstrate the technique for the generation of e+ e- --> q qbar + 1 jet events using the MC@NLO formalism. I will also discuss what the next steps to implement ARCANE reweighting for processes relevant for LHC experiments might look like.