A Track Reconstructing Low-latency Trigger Processor for High-energy Physics

The detection and analysis of the large number of particles emerging from high-energy collisions between atomic nuclei is a major challenge in experimental heavy-ion physics. Efficient trigger systems help to focus the analysis on relevant events. A primary objective of the Transition Radiation Detector of the ALICE experiment at the LHC is to trigger on high-momentum electrons. In this thesis, a trigger processor is presented that employs massive parallelism to perform the required online event reconstruction within 2 µs to contribute to the Level-1 trigger decision. Its three-stage hierarchical architecture comprises 109 nodes based on FPGA technology. Ninety processing nodes receive data from the detector front-end at an aggregate net bandwidth of 2.16 Tbps via 1080 optical links. Using specifically developed components and interconnections, the system combines high bandwidth with minimum latency. The employed tracking algorithm three-dimensionally reassembles the track segments found in the detector's drift chambers based on explicit value comparisons, calculates the momentum of the originating particles from the course of the reconstructed tracks, and finally leads to a trigger decision. The architecture is capable of processing up to 20,000 track segments in less than 2 µs with high detection efficiency and reconstruction precision for high-momentum particles. As a result, this thesis shows how a trigger processor performing complex online track reconstruction within tight real-time requirements can be realized. The presented hardware has been built and is in continuous data taking operation in the ALICE experiment.