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

E. Bartz, G. Boudoul, R. Bucci, J. Chaves, E. Clement, D. Cranshaw, S. Dutta, Y. Gershtein, R. Glein, K. Hahn, E. Halkiadakis, M. Hildreth, S. Kyriacou, K. Lannon, A. Lefeld, Y. Liu, E. MacDonald, N. Pozzobon, A. Ryd, K. SalyerP. Shields, L. Skinnari, K. Stenson, R. Stone, C. Strohman, K. Sung, Z. Tao, M. Trovato, K. Ulmer, S. Viret, B. Winer, P. Wittich*, B. Yates, M. Zientek

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Article numberP06024
JournalJournal of Instrumentation
Issue number6
StatePublished - Jun 2020


  • Particle tracking detectors (Solid-state detectors)
  • Pattern recognition
  • Trigger algorithms
  • Trigger concepts and systems (hardware and software)
  • calibration and fitting methods
  • cluster finding

ASJC Scopus subject areas

  • Instrumentation
  • Mathematical Physics


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