The upcoming Belle II particle collider experiment is the most modern particle detector experiment in the world. It will target a world record Luminosity, which corresponds to the number of particle collisions over time. The resulting amount of data to be saved for later analysis of the experiment is simply too huge to get across the data transmission lines. Fortunately, a huge chunk of data is produced by effects that are not important for the experiment, as they don’t result in new knowledge. Identifying this data early on allows discarding uninteresting data, while saving only relevant data. This approach solves the data transmission problem.
At the ITIV we are researching, implementing and integrating so called trigger mechanisms on FPGAs for Belle II. These are deciding over the readout of the detector during the collisions. A novel component is the Neural Network Trigger, which uses neural networks to estimate the origin of particle collisions. This trigger is unique and was specially developed for the Belle II experiment. Because of this new development there are no existing slow control mechanism.
Task of the Thesis
The task of this work is to develop and implement a slow control for the Neural Network Trigger.
- Familiarization phase
- Orientation in design of the Neuronal Network Trigger
- Familiarization with Slow Control Monitoring
- Familiarization with VHDL
- Concept- and design phase
- Develop a Concept for Slow Control Monitoring of the Neuronal Network Trigger
- Implementation phase
- Implementation of the Concept
- Evaluation of Slow Control Monitoring
- Creation of a documentation covering the topics described above
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