Speaker
Description
Prototypes of electromagnetic and hadronic imaging calorimeters
developed and operated by the CALICE collaboration provide an
unprecedented wealth of highly granular data of hadronic showers for a
variety of active sensor elements and different absorber materials. In
this presentation, we discuss detailed measurements of the spatial and
the time structure of hadronic showers to characterise the different
stages of hadronic cascades in the calorimeters, which are then
confronted with GEANT4-based simulations using different hadronic
physics models. These studies also extend to the two different absorber
materials, steel and tungsten, used in the prototypes. The high
granularity of the detectors is exploited in the reconstruction of
hadronic energy, both in individual detectors and combined
electromagnetic and hadronic systems, making use of software
compensation and semi-digital energy reconstruction. The results include
new simulation studies that predict the reliable operation of granular
calorimeters. Further we show how granularity and the application of
multivariate analysis algorithms enable the separation of close-by
particles. We will report on the performance of these reconstruction
techniques for different electromagnetic and hadronic calorimeters, with
silicon, scintillator and gaseous active elements. Granular calorimeters
are also an ideal testing ground for the application of machine learning
techniques. We will outline how these techniques are applied to CALICE
data and in the CALICE simulation framework.
