Speaker
Description
A challenge that industrial particle accelerators face is the high amounts of noise in sensor readings. This noise obscures essential beam diagnostic and operation data, limiting the amount of information that is relayed to machine operators and beam instrumentation engineers. Machine learning-based techniques have shown great promise in isolating noise patterns while preserving high-fidelity signals, enabling more accurate diagnostics and performance tuning. Our work focuses on investigating the challenges associated with the implementation of a noise reduction autoencoder that operates in real time on a Field Programmable Gate Array, which we do by creating firmware to run on a Xilinx ZCU104 evaluation kit with the intention of being deployed on industrial particle accelerators in the near future.
| Abstract Category | Measurement and Control |
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