Energy reconstruction of hadrons in highly granular combined ECAL and HCAL systems
This paper discusses the hadronic energy reconstruction of two combined electromagnetic and hadronic calorimeter systems using physics prototypes of the CALICE collaboration: the silicon-tungsten electromagnetic calorimeter (Si-W ECAL) and the scintillator-SiPM based analog hadron calorimeter (AHCAL); and the scintillator-tungsten electromagnetic calorimeter (ScECAL) and the AHCAL. These systems were operated in hadron beams at CERN and FNAL, permitting the study of the performance in combined ECAL and HCAL systems. Two techniques for the energy reconstruction are used, a standard reconstruction based on calibrated sub-detector energy sums, and one based on a software compensation algorithm making use of the local energy density information provided by the high granularity of the detectors. The software compensation-based algorithm improves the hadronic energy resolution by up to 30% compared to the standard reconstruction. The combined system data show comparable energy resolutions to the one achieved for data with showers starting only in the AHCAL and therefore demonstrate the success of the inter-calibration of the different sub-systems, despite of their different geometries and different readout technologies.
Calorimetric Measurement of Multi-TeV Muons via Deep Regression
The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momentum at very high energy, where an estimate of the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient for achieving good momentum resolution when detecting, e.g., a narrow, high mass resonance decaying to a muon pair. In this work we study the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by exploiting spatial information of the clusters of energy from radiated photons in a regression task. The use of a task-specific deep learning architecture based on convolutional layers allows us to treat the problem as one akin to image reconstruction, where images are constituted by the pattern of energy released in successive layers of the calorimeter. A measurement of muon energy with better than 20% relative resolution is shown to be achievable for ultra-TeV muons.
Initial Study of Hadronic Energy Resolution in the Analog HCAL and the Complete CALICE Setup
The hadronic energy resolution of the CALICE analog hadron calorimeter and of the complete CALICE setup, consisting of a SiW electromagnetic calorimeter, an analog scintillatorsteel hadron calorimeter and an analog scintillator-steel tail catcher, has been studied using data taken in 2007 at CERN. To optimize the energy resolution for single hadrons by taking advantage of the high granularity and segmentation of these calorimeters, a simple signal weighting procedure based on the density of deposited energy in each cell is studied. These initial studies yield a stochastic term of the resolution of approximately 50%/ √ E in the case of the HCAL stand alone and for the complete setup. This corresponds to an improvement of roughly 25% and 18% compared to the resolution obtained without density dependent weighting for the HCAL and the complete setup, respectively. ∗Corresponding authors: Katja Seidel (kseidel@mpp.mpg.de), Frank Simon (frank.simon@universe-cluster.de)