Identification of the hadronic interaction layer in a highly granular calorimeter by multivariate techniques

Abstract

Calorimetry in future and upgrade collider detectors has a clear overall trend towards high granularity, both laterally and longitudinally. This trend is a requirement for the full exploitation of particle flow algorithms, which reconstruct individual particles using the subdetector that provides the best resolution for this specific particle. The increased level of detail in the event topologies due to higher segmentation of the calorimeter provides additional handles for several calorimetric measurements, such as the identification of the hadronic interaction layer, which is not possible for calorimeters with traditional tower geometry. Here, the power of using multivariate statistical techniques in the identification of the hadronic interaction layer in a highly segmented calorimeter is investigated.