用神经形态计算无监督追踪粒子轨迹，抗干扰能力强。
Unsupervised Particle Tracking with Neuromorphic Computing
- 基于脉冲时序依赖可塑性，无监督学习延迟与突触权重。
- 在强噪声下仍能准确识别带电粒子轨迹。
- 适合未来高能物理实验的实时低功耗追踪需求。
我们研究了一种神经网络架构，通过脉冲时序依赖可塑性规则，无监督地学习延迟和突触权重，以识别对撞机探测器中带电粒子的轨迹。模型接收根据紧凑缪子螺线管二期探测器几何结构建模的时间编码位置信息。结果表明，该脉冲神经网络可在存在显著误触发或组合背景噪声的情况下，完全无监督地成功识别带电粒子留下的信号。这些成果为神经形态计算在粒子追踪中的应用开辟了道路，激励进一步研究其在未来的高能物理实验中实现实时、低功耗追踪的潜力。
原文摘要 · Abstract (English)
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.
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