TUTCRIS - Tampereen teknillinen yliopisto


Big data analytics for the Future Circular Collider reliability and availability studies



Otsikko22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP2016)
KustantajaIOP Publishing
DOI - pysyväislinkit
TilaJulkaistu - 23 marraskuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Computing in High Energy and Nuclear Physics -
Kesto: 1 tammikuuta 1900 → …


Nimi Journal of Physics: Conference Series
ISSN (painettu)1742-6588
ISSN (elektroninen)1742-6596


ConferenceInternational Conference on Computing in High Energy and Nuclear Physics
Ajanjakso1/01/00 → …


Responding to the European Strategy for Particle Physics update 2013, the Future Circular Collider study explores scenarios of circular frontier colliders for the post-LHC era. One branch of the study assesses industrial approaches to model and simulate the reliability and availability of the entire particle collider complex based on the continuous monitoring of CERN's accelerator complex operation. The modelling is based on an in-depth study of the CERN injector chain and LHC, and is carried out as a cooperative effort with the HL-LHC project. The work so far has revealed that a major challenge is obtaining accelerator monitoring and operational data with sufficient quality, to automate the data quality annotation and calculation of reliability distribution functions for systems, subsystems and components where needed. A flexible data management and analytics environment that permits integrating the heterogeneous data sources, the domain-specific data quality management algorithms and the reliability modelling and simulation suite is a key enabler to complete this accelerator operation study. This paper describes the Big Data infrastructure and analytics ecosystem that has been put in operation at CERN, serving as the foundation on which reliability and availability analysis and simulations can be built. This contribution focuses on data infrastructure and data management aspects and presents case studies chosen for its validation.

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