用类脑计算处理粒子探测器光信号，实现快速低功耗能量与位置估计。
Neuromorphic Readout for Hadron Calorimeters
- 将光子时间分布转为脉冲信号，用全连接脉冲神经网络解码。
- 无需分割探测材料，直接提取能量、位置及发光分布的拓扑信息。
- 可集成于纳米光子器件，适合高能物理实时数据处理。
我们模拟了强子撞击均匀铅钨酸盐（PbWO4）量热计的过程，研究光敏传感器阵列检测到的光产额及其时间结构，如何通过类脑计算系统进行处理。模型将时间光子分布编码为脉冲序列，采用全连接脉冲神经网络估计总沉积能量、光发射的位置与空间分布。提取的特征提供了关于闪烁体中簇射发展的有价值拓扑信息，且无需对活性介质进行分割。还讨论了基于III-V族半导体纳米线的潜在纳米光子实现方案，具有速度快、功耗低的优势。
原文摘要 · Abstract (English)
We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient.
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