Neuromorphic Readout for Hadron Calorimeters
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Lupi, Enrico Abhishek Aehle, Max Awais, Muhammad Breccia, Alessandro Carroccio, Riccardo Chen, Long Das, Abhijit De Vita, Andrea Dorigo, Tommaso Gauger, Nicolas R. Keidel, Ralf Kieseler, Jan Mikkelsen, Anders Nardi, Federico Nguyen, Xuan Tung Sandin, Fredrik Schmidt, Kylian Vischia, Pietro Willmore, Joseph |
| author_facet | Lupi, Enrico Abhishek Aehle, Max Awais, Muhammad Breccia, Alessandro Carroccio, Riccardo Chen, Long Das, Abhijit De Vita, Andrea Dorigo, Tommaso Gauger, Nicolas R. Keidel, Ralf Kieseler, Jan Mikkelsen, Anders Nardi, Federico Nguyen, Xuan Tung Sandin, Fredrik Schmidt, Kylian Vischia, Pietro Willmore, Joseph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neuromorphic Readout for Hadron Calorimeters Lupi, Enrico Abhishek Aehle, Max Awais, Muhammad Breccia, Alessandro Carroccio, Riccardo Chen, Long Das, Abhijit De Vita, Andrea Dorigo, Tommaso Gauger, Nicolas R. Keidel, Ralf Kieseler, Jan Mikkelsen, Anders Nardi, Federico Nguyen, Xuan Tung Sandin, Fredrik Schmidt, Kylian Vischia, Pietro Willmore, Joseph High Energy Physics - Experiment Emerging Technologies Machine Learning Neural and Evolutionary Computing 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. |
| title | Neuromorphic Readout for Hadron Calorimeters |
| topic | High Energy Physics - Experiment Emerging Technologies Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2502.12693 |