PoS - Proceedings of Science
Volume 398 - The European Physical Society Conference on High Energy Physics (EPS-HEP2021) - T12: Detector R&D and Data Handling
Preparation for ALICE data processing and analysis in LHC Run 3
G. Eulisse*  on behalf of the ALICE collaboration
Full text: pdf
Pre-published on: February 10, 2022
Published on: May 12, 2022
After the ALICE Long Shutdown 2 detector upgrades, including a new silicon tracker and a GEM-based readout for the TPC, the experiment will operate during LHC Run 3 at a peak Pb-Pb collision rate of 50 kHz, about 50 times higher than in previous running periods. To maximise the significance of physics signals with low S/B ratios for which triggering is not possible, all events will be read out and written to permanent storage without any selective trigger. In order to minimise the costs and computing time of the online and offline systems, data volume reduction is performed synchronous with data taking on the newly installed Online/Offline facility O2. The facility consists of two types of compute nodes, the First Level Processors (FLP) and the Event Processing Nodes (EPN). Each FLP receives data from parts of individual detectors, performs a first level of data compression by zero suppression as well as calibration tasks, and sends its output to the EPNs over an Infiniband network. Using the EPN’s CPU cores and GPUs, data is reconstructed and further compressed. Moreover, data for detector calibration is created. Online data processing is followed by offline reconstruction passes using fully calibrated data producing the input for data analysis (AOD). In addition, large samples of simulated data as input for detector response and performance studies will be produced.
Here we describe the data processing chain and give an overview of the design choices and imple- mentations for the newly developed software frameworks, which can cope with the unprecedented data rates and volumes. The status of the preparation for data processing and analysis in view of the first physics runs in 2022 is presented.
DOI: https://doi.org/10.22323/1.398.0824
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