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FPGA-based tracking for the CMS Level-1 trigger using the tracklet algorithm

The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High-Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment. A central component to allow efficient operation under these conditions is the reconstruction of charge...

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Detalles Bibliográficos
Autores principales: Bartz, E., Boudoul, G., Bucci, R., Chaves, J., Clement, E., Cranshaw, D., Dutta, S., Gershtein, Y., Glein, R., Hahn, K., Halkiadakis, E., Hildreth, M., Kyriacou, S., Lannon, K., Lefeld, A., Liu, Y., MacDonald, E., Pozzobon, N., Ryd, A., Salyer, K., Shields, P., Skinnari, L., Stenson, K., Stone, R., Strohman, C., Sung, K., Tao, Z., Trovato, M., Ulmer, K., Viret, S., Winer, B., Wittich, P., Yates, B., Zientek, M.
Lenguaje:eng
Publicado: 2019
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1748-0221/15/06/P06024
http://cds.cern.ch/record/2696252
Descripción
Sumario:The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High-Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment. A central component to allow efficient operation under these conditions is the reconstruction of charged particle trajectories and their inclusion in the hardware-based trigger system. There are many challenges involved in achieving this: a large input data rate of about 20–40 Tb/s processing a new batch of input data every 25 ns, each consisting of about 15,000 precise position measurements and rough transverse momentum measurements of particles (“stubs”); performing the pattern recognition on these stubs to find the trajectories; and producing the list of trajectory parameters within 4 μs. This paper describes a proposed solution to this problem, specifically, it presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The results of an end-to-end demonstrator system, based on Xilinx Virtex-7 FPGAs, that meets timing and performance requirements are presented along with a further improved, optimized version of the algorithm together with its corresponding expected performance.