The ALICE Data Quality Monitoring: qualitative and quantitative review of three years of operations

20th International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Von Haller, Barthélémy^1 ; Bellini, Francesca^2 ; Telesca, Adriana^1 ; Foka, Yiota^3

CERN, Physics Department, Geneva, Switzerland^1

INFN, Università Degli Studi, Bologna, Italy^2

GSI, Darmstadt, Germany^3

关键词: Complete information;    Data quality monitoring;    Heavy ion collision;    High quality data;    Large Hadron collider LHC;    Large ion collider experiments;    Online feedback;    Performance requirements;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/513/1/012038/pdf
DOI  :  10.1088/1742-6596/513/1/012038

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

ALICE (A Large Ion Collider Experiment) is a detector designed to study the physics of strongly interacting matter produced in heavy-ion collisions at the CERN Large Hadron Collider (LHC). Due to the complexity of ALICE in terms of number of detectors and performance requirements, Data Quality Monitoring (DQM) plays an essential role in providing online feedback on the data being recorded. It intends to provide shifters with precise and complete information to quickly identify problems, and as a consequence to ensure acquisition of high quality data. This paper presents a review of the ALICE DQM system during the first three years of LHC operations from a quantitative and qualitative point of view. We start by presenting the DQM software and tools before moving on to the various analyses carried out. An overview of the produced monitoring quantities is given, presenting the diversity of usage and flexibility of the DQM. Well-prepared shifters and experts, in addition to a precise organisation, were required to ensure smooth and successful operations. The description of the measures taken to ensure both aspects and an account of the DQM shifters' job are followed by a summary of the evolution of the system. We then give a quantitative review of the final setup of the system used during the whole year 2012. We conclude the paper with use cases where the DQM proved to be very valuable, scalable and efficient and with the plans for the coming years.