Abstract
We simulate hadrons impinging on a homogeneous lead tungstate (PbWO4) calorimeter using GEANT4 software to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient.
Max Aehle
Affiliated Researcher
Long Chen
Affiliated Researcher
Tommaso Dorigo
Principal Investigator/Professor
Ralf Keidel
Affiliated Researcher
Jan Kieseler
Principal Investigator/Professor
Federico Nardi
Affiliated Researcher
Fredrik Sandin
Principal Investigator/Professor
