Speeding up particle track reconstruction using a parallel Kalman filter algorithm

@article{Lantz2020SpeedingUP,
  title={Speeding up particle track reconstruction using a parallel Kalman filter algorithm},
  author={Steven R. Lantz and K. Mcdermott and Michael Reid and Daniel Riley and Peter Wittich and Sophie Berkman and Giuseppe Benedetto Cerati and Matti J. Kortelainen and Allison Reinsvold Hall and Peter Elmer and Bei Wang and Leonardo Giannini and Vyacheslav Krutelyov and Mario Masciovecchio and Matevvz Tadel and Frank Wurthwein and Avraham Yagil and Brian Gravelle and Boyana Norris Cornell University and Ithaca and Ny and Usa 14853 and Fermi National Accelerator Laboratory and Batavia and Il and Usa 60510 and Princeton University and Princeton and Nj and Usa 08544 and U. C. San Diego and La Jolla and Ca and Usa 92093 and University of Oregon and Eugene and Or and Usa 97403},
  journal={arXiv: Instrumentation and Detectors},
  year={2020}
}
One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core… 
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