On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. We demonstrate that neural networks embedded in front-end electronics can infer charged-particle kinematic parameters from a single silicon layer, regressing hit positions and incident angles with calibrated uncertainties while satisfying strict constraints on precision, latency, and silicon area.

arXiv:2602.15946 [physics.ins-det; hep-ex]