000002594 001__ 2594
000002594 005__ 20210809114916.0
000002594 037__ $$aBELLE2-PTHESIS-2021-015
000002594 041__ $$aeng
000002594 100__ $$aSteffen Baehr
000002594 245__ $$aReal-Time Trigger and online Data Reduction based on Machine Learning Methods for Particle Detector Technology
000002594 260__ $$aKarlsruhe$$bKIT$$c2020
000002594 300__ $$a290
000002594 500__ $$aPresented on 16 07 2020
000002594 502__ $$aPhD$$bKarlsruhe, KIT$$c2020
000002594 520__ $$aModern particle accelerator experiments are producing immense amounts of data online during their operation. Storing the entire amount of generated data is quickly exceeding reasonable budgets for the data readout infrastructure. This problem is traditionally addressed by using a combination of trigger and data reduction mechanisms that are located close to the respective detectors to facilitate a reduction of the data rates as early in the process as possible. Meanwhile, traditional approaches to these systems are struggling with achieving an efficient reduction for modern experiments such as Belle II. The reason for this lies in the complex observed distributions of background, or unwanted, events. This situation is enhanced by the unknown characteristics of both the accelerator and detector before reaching high luminosity operation. A robust and flexible algorithmic alternative is thus required to address this problem. This can be provided by using an approach based on machine learning. Since such trigger and data reduction systems are operated under tight constraints such as small latency budgets, a high number of required data transmission connections and general real-time processing, FPGAs are used as a technological basis for the implementation. Within this thesis, several approaches based on machine learning methods were developed for FPGAs to fulfil the challenges present at the Belle II experiment. These systems are presented and discussed throughout this thesis.
000002594 700__ $$aJuergen Becker$$edir.
000002594 8560_ $$fsteffen.baehr@kit.edu
000002594 8564_ $$uhttps://docs.belle2.org/record/2594/files/BELLE2-PTHESIS-2021-015.pdf
000002594 980__ $$aTHESIS