LONGITUDINALLY-INVARIANT K-PERPENDICULAR-TO-CLUSTERING ALGORITHMS FOR HADRON-HADRON COLLISIONS

We propose a version of the QCD-motivated ''k(perpendicular-to)'' jet-clustering algorithm for hadron-hadron collisions which is invariant under boosts along the beam directions, This leads to improved factorization properties and closer correspondence to experimental practice at hadron colliders. We examine alternative definitions of the resolution variables and cluster recombination scheme, and show that the algorithm can be implemented efficiently on a computer to provide a full clustering history of each event. Using simulated data at square-root S = 1.8 TeV, we study the effects of calorimeter segmentation, hadronization and the soft underlying event, and compare the results with those obtained using a conventional cone-type algorithm.