We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter 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.
@article{arxiv.2502.12693,
title = {Neuromorphic Readout for Hadron Calorimeters},
author = {Enrico Lupi and Abhishek and Max Aehle and Muhammad Awais and Alessandro Breccia and Riccardo Carroccio and Long Chen and Abhijit Das and Andrea De Vita and Tommaso Dorigo and Nicolas R. Gauger and Ralf Keidel and Jan Kieseler and Anders Mikkelsen and Federico Nardi and Xuan Tung Nguyen and Fredrik Sandin and Kylian Schmidt and Pietro Vischia and Joseph Willmore},
journal= {arXiv preprint arXiv:2502.12693},
year = {2025}
}