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.
Mass Loss History of Evolved Stars
We provide high-resolution maps of the circumstellar dust shells of several dozen Asymptotic Giant Branch (AGB) stars using data from the Spitzer space telescope Multiband Imaging Photometer (MIPS) imaged at 70μm. AGB stars are the major contributors of chemical elements such as carbon, oxygen and silicon, which are essential to the existence of life in the universe, through mass loss processes that take place at the surface of the star. We probe the spatial distribution of cold (∼40 K) dust grains in order to trace the history of mass loss from the observed radial density variation in these shells. Our images illustrate that different morphologies may evolve because AGB stars can already interact with the interstellar medium (ISM) that surrounds them even at these early stages of the AGB mass loss history. Relative motion of the star as well as the ISM with respect to the local environment may be of particular importance for shell structure evolution.