Unsupervised Particle Tracking with Neuromorphic Computing
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908312947130368 |
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| author | Coradin, Emanuele Cufino, Fabio Awais, Muhammad Dorigo, Tommaso Lupi, Enrico Porcu, Eleonora Raj, Jinu Sandin, Fredrik Tosi, Mia |
| author_facet | Coradin, Emanuele Cufino, Fabio Awais, Muhammad Dorigo, Tommaso Lupi, Enrico Porcu, Eleonora Raj, Jinu Sandin, Fredrik Tosi, Mia |
| contents | We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase II detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits. These results open the way to applications of neuromorphic computing to particle tracking, motivating further studies into its potential for real-time, low-power particle tracking in future high-energy physics experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Particle Tracking with Neuromorphic Computing Coradin, Emanuele Cufino, Fabio Awais, Muhammad Dorigo, Tommaso Lupi, Enrico Porcu, Eleonora Raj, Jinu Sandin, Fredrik Tosi, Mia High Energy Physics - Experiment Emerging Technologies Machine Learning Neural and Evolutionary Computing I.2; I.5; J.2 We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase II detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits. These results open the way to applications of neuromorphic computing to particle tracking, motivating further studies into its potential for real-time, low-power particle tracking in future high-energy physics experiments. |
| title | Unsupervised Particle Tracking with Neuromorphic Computing |
| topic | High Energy Physics - Experiment Emerging Technologies Machine Learning Neural and Evolutionary Computing I.2; I.5; J.2 |
| url | https://arxiv.org/abs/2502.06771 |