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Unsupervised Particle Tracking with Neuromorphic Computing
Dipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, Italy.ORCID iD: 0009-0007-4380-7866
Dipartimento di Fisica, Università di Bologna, Via Irnerio, 40126 Bologna, Italy.ORCID iD: 0009-0000-6310-469X
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Dipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Padova, Via F. Marzolo 8, 35131 Padova, Italy.ORCID iD: 0009-0001-3665-9507
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Istituto Nazionale di Fisica Nucleare, Sezione di Padova, Via F. Marzolo 8, 35131 Padova, Italy.ORCID iD: 0000-0002-1659-8727
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2025 (English)In: Particles, E-ISSN 2571-712X, Vol. 8, no 2, article id 40Article in journal (Refereed) Published
Abstract [en]

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-2 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, opening the way to applications of neuromorphic computing to particle tracking. The presented results motivate further studies investigating neuromorphic computing as a potential solution for real-time, low-power particle tracking in future high-energy physics experiments.

Place, publisher, year, edition, pages
MDPI, 2025. Vol. 8, no 2, article id 40
Keywords [en]
particle detectors, particle tracking, neuromorphic computing, unsupervised learning, spiking neural networks, genetic algorithms
National Category
Artificial Intelligence
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-113926DOI: 10.3390/particles8020040ISI: 001514899300001OAI: oai:DiVA.org:ltu-113926DiVA, id: diva2:1979098
Funder
Knut and Alice Wallenberg Foundation
Note

Validerad;2025;Nivå 1;2025-06-30 (u5);

Full text license: CC BY 4.0;

Funder: Jubileumsfonden;

Available from: 2025-06-30 Created: 2025-06-30 Last updated: 2025-06-30Bibliographically approved

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Khan, AwaisDorigo, TommasoSandin, Fredrik

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