Unsupervised Particle Tracking with Neuromorphic Computing
This work addresses particle tracking for high-energy physics experiments, offering a potential real-time, low-power solution, though it appears incremental as it applies neuromorphic computing to a specific domain.
The paper tackled the problem of identifying charged particle trajectories in a particle collider detector using an unsupervised spiking neural network with spike-time-dependent plasticity, achieving successful identification of signal hits in the presence of conspicuous noise.
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.