Unsupervised Particle Tracking with Neuromorphic Computing

TL;DR

This paper demonstrates that a spiking neural network using unsupervised learning can effectively identify charged particle trajectories in noisy detector data, highlighting neuromorphic computing's potential for real-time physics experiments.

Contribution

It introduces a novel application of neuromorphic, spike-time-dependent plasticity-based neural networks for unsupervised particle tracking in high-energy physics detectors.

Findings

Successful unsupervised identification of particle signals amidst noise

Potential for real-time, low-power particle tracking applications

Demonstrated applicability to CMS Phase II detector geometry

Abstract

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.