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Neuromorphic Readout for Hadron Calorimeters

Lupi, Enrico ; Abhishek ; Aehle, Max ; Awais, Muhammad ; Breccia, Alessandro ; Carroccio, Riccardo ; Chen, Long ; Das, Abhijit LU orcid ; De Vita, Andrea and Dorigo, Tommaso , et al. (2025) In Particles 8(2).
Abstract

We simulate hadrons impinging on a homogeneous lead tungstate ((Formula presented.)) 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... (More)

We simulate hadrons impinging on a homogeneous lead tungstate ((Formula presented.)) 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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
calorimeter, III-V semiconductor nanowires, machine learning, nanophotonics, nanowire, neuromorphic computing, particle detector, spiking neural networks
in
Particles
volume
8
issue
2
article number
52
publisher
MDPI AG
external identifiers
  • scopus:105009272866
ISSN
2571-712X
DOI
10.3390/particles8020052
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 by the authors.
id
b2a69295-70cf-4087-b1e7-a9a895373e70
date added to LUP
2025-12-16 11:04:19
date last changed
2025-12-16 11:05:35
@article{b2a69295-70cf-4087-b1e7-a9a895373e70,
  abstract     = {{<p>We simulate hadrons impinging on a homogeneous lead tungstate ((Formula presented.)) 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.</p>}},
  author       = {{Lupi, Enrico and Abhishek and Aehle, Max and Awais, Muhammad and Breccia, Alessandro and Carroccio, Riccardo and Chen, Long and Das, Abhijit and De Vita, Andrea and Dorigo, Tommaso and Gauger, Nicolas Ralph and Keidel, Ralf and Kieseler, Jan and Mikkelsen, Anders and Nardi, Federico and Nguyen, Xuan Tung and Sandin, Fredrik and Schmidt, Kylian and Vischia, Pietro and Willmore, Joseph}},
  issn         = {{2571-712X}},
  keywords     = {{calorimeter; III-V semiconductor nanowires; machine learning; nanophotonics; nanowire; neuromorphic computing; particle detector; spiking neural networks}},
  language     = {{eng}},
  number       = {{2}},
  publisher    = {{MDPI AG}},
  series       = {{Particles}},
  title        = {{Neuromorphic Readout for Hadron Calorimeters}},
  url          = {{http://dx.doi.org/10.3390/particles8020052}},
  doi          = {{10.3390/particles8020052}},
  volume       = {{8}},
  year         = {{2025}},
}