12 – 14 de jul. de 2021
Fuso horário America/Sao_Paulo

Computing systems for ALICE/LHC: run3 and beyond

13 de jul. de 2021 14:00
20m

Palestrante

Rafael Peretti Pezzi (UFRGS & Subatech)

Descrição

The ALICE experiment at the LHC/CERN is being upgraded to further improve its capacity to characterize the Quark-Gluon Plasma (QGP) with improved vertexing resolution and exploitation of the higher luminosity of the upgraded LHC. Besides several instrumental updates, including new electronics and detector systems, the On-line and Off-line computing system of ALICE (ALICE O2) is being commissioned to replace the previous software stacks. The O2 framework is designed to provide high throughput data acquisition and real-time reconstruction with high compression to reduce the 3 TB/s of data foreseen to be generated by the ALICE detector during the 50 kHz PbPb collisions period down to 90 GB/s of physics-analysis objects for storage. Other responsibilities of the ALICE O2 computing system includes detector simulation, generation of Monte Carlo datasets and data analysis.

GEFAE-IF/UFRGS from Porto Alegre/RS/Brazil is an experimental group with recent experience in the development, testing and assembly of the MFT detector (Muon Forward Tracker). The MFT was recently installed in the ALICE experiment at the LHC/CERN and is currently under commissioning in view of the next running period of the LHC. The group is committed to the operation of the MFT and to the development of the related components of the ALICE O2 computing system. This includes the development and implementation of a forward tracking model for reconstruction and analysis of forward tracks. The group has also conceived a machine learning interface for track-reconstruction.

The ALICE O2 framework and its forward tracking code is being put into practice in performance studies and layout optimization of the post-LS4 (Long Shutdown 4) heavy ion experiment at the LHC, which is expected to replace the upgraded ALICE detector by 2032. The group is interested into further advance its experience in simulation, machine learning and forward tracking by contributing to the understanding of detector and physics performances and explore the limits of forward tracking in the next generation heavy-ion experiment at the LHC.

Key Words LHC, ALICE, MFT, ALICE3

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