Speaker
Description
Hadronic calorimeters are a key part of high energy physics experiments. Traditionally, they rely on high granularity to improve performances, but this leads to various challenges in terms of cost, energy consumption and output data volume. Moreover, current detectors do not have the capability of exploiting temporal information of the shower development, as the time frame for pattern detection is condensed to sub-nanoseconds due to the particles' speed. Neuromorphic architectures might help with overcoming these limitations.
We explore a neuromorphic approach to calorimeter readout. Hadrons interacting with a homogeneous lead-tungstate (PbWO₄) calorimeter are simulated and the resulting time-dependent light signals, captured by a dense array of photodetectors, are encoded into spike trains to serve as input to a fully connected spiking neural network (SNN). This architecture is trained to reconstruct key physical quantities, including the total energy deposited and topological information about the events. The model performs effectively in both single- and multi-task regression settings, producing consistent results in both scenarios.
Finally, we discuss the feasibility of implementing such a readout system using nanophotonic hardware based on III-V semiconductor nanowires, highlighting a pathway toward ultra-fast, energy-efficient calorimeter readout.