Neuromorphic Readout for Hadron Calorimeters

Neuromorphic Readout for Hadron Calorimeters

We simulate hadrons impinging on a homogeneous lead tungstate (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>PbWO</mi><mn>4</mn></msub></semantics></math><...

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Bibliographic Details
Main Authors:	Enrico Lupi, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Abhijit Das, Andrea De Vita, Tommaso Dorigo, Nicolas Ralph Gauger, Ralf Keidel, Jan Kieseler, Anders Mikkelsen, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore
Format:	Article
Language:	English
Published:	MDPI AG 2025-05-01
Series:	Particles
Subjects:	machine learning neuromorphic computing calorimeter particle detector spiking neural networks nanowire
Online Access:	https://www.mdpi.com/2571-712X/8/2/52
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Summary:	We simulate hadrons impinging on a homogeneous lead tungstate (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>PbWO</mi><mn>4</mn></msub></semantics></math></inline-formula>) 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.
ISSN:	2571-712X