Does SUSY have friends? A new approach for LHC event analysis
Anna Mullin, Holly Pacey, Michael Parker, Martin White, Sarah Williams
We present a novel technique for the analysis of proton-proton collision events from the ATLAS and CMS experiments at the Large Hadron Collider. For a given final state and choice of kinematic variables, we build a graph network in which the individual events appear as weighted nodes, with edges between events defined by their distance in kinematic space. We then show that it is possible to calculate local metrics of the network that serve as event-by-event variables for separating signal and background processes, and we evaluate these for a number of different networks that are derived from different distance metrics. Using supersymmetric electroweakino and stop production as examples, we construct prototype analyses that take account of the fact that the number of simulated Monte Carlo events used in an LHC analysis may differ from the number of events expected in the LHC dataset, allowing one to derive an accurate background estimate for a particle search at the LHC. We show that the network variables offer significantly greater discrimination between signal and background processes than the original kinematic variables used in the definition of the network.