Neuromorphic Readout for Hadron Calorimeters

We simulate hadrons impinging on a homogeneous lead tungstate (PbWO4)
calorimeter using GEANT4 software to investigate how the resulting light yield and its tem-
poral 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 de-
posited 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