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Abstract

ALICE is one of the four major experiments at the Large Hadron Collider (LHC). It is the dedicated heavy-ion experiment and therefore primarily examines the Quark–Gluon Plasma. In order to prepare for the running conditions of 50 kHz lead-lead interactions at the LHC after the Long Shutdown 2 (2018–2021), an extensive upgrade program is carried out. The goal of the upgrade is a continuous readout of the TPC without the need of a trigger. It is essential to reduce the enormous data rate of 3.7 TB/s, generated by the upgraded detector, already during the data taking by a factor of about 60. Otherwise the data volume would exceed the expected available bandwidth and storage capabilities. In this thesis, an online Cluster Finder (CF) was developed and implemented for FPGAs which processes the whole data volume in real-time during the read out. This is the first step in the data reduction sequence which achieves already a factor of about 5 by keeping only physically relevant information and making use of a better suited data format. In addition to the CF, also the whole data preparation chain was designed and implemented to decode the input data stream, to resort the individual channels to allow for cluster finding and to correct the detector effects in the input signals. All modules which were implemented were extensively simulated to verify their proper functionality. With this, the complete processing chain within the FPGAs was prepared and validated.