Neuromorphic Readout for Hadron Calorimeters

TL;DR

This paper explores a neuromorphic computing approach to analyze light signals from a lead-tungstate calorimeter, enabling energy and position estimation of particle showers without segmentation, with potential for fast, energy-efficient nanophotonic implementation.

Contribution

It introduces a neuromorphic readout system for calorimeters that encodes photon data as spike trains and estimates shower properties without segmentation, proposing a novel nanophotonic implementation.

Findings

Successful simulation of light yield and temporal structure analysis

Effective estimation of energy and spatial distribution using spiking neural networks

Discussion of a nanophotonic implementation for fast, energy-efficient processing

Abstract

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.