Neural networks integrated into silicon sensor front-end electronics can regress charged-particle hit positions and angles with calibrated uncertainties from single-layer data while satisfying hardware constraints on precision, latency, and area.
The trained QKeras models are translated into synthe- sizable C++ usinghls4ml[22, 23]
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On-chip probabilistic inference for charged-particle tracking at the sensor edge
Neural networks integrated into silicon sensor front-end electronics can regress charged-particle hit positions and angles with calibrated uncertainties from single-layer data while satisfying hardware constraints on precision, latency, and area.