Published November 2018 | Version public
Book Section - Chapter

TrackML: A High Energy Physics Particle Tracking Challenge

  • 1. ROR icon Lawrence Berkeley National Laboratory
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon European Organization for Nuclear Research
  • 4. ROR icon University of Geneva
  • 5. ROR icon University of Massachusetts Boston
  • 6. ROR icon Laboratoire de Physique des 2 Infinis Irène Joliot-Curie
  • 7. ROR icon Laboratoire de Physique Nucléaire et de Hautes Énergies
  • 8. ROR icon National Research University Higher School of Economics

Abstract

To attain its ultimate discovery goals, the luminosity of the Large Hadron Collider at CERN will increase so the amount of additional collisions will reach a level of 200 interaction per bunch crossing, a factor 7 w.r.t the current (2017) luminosity. This will be a challenge for the ATLAS and CMS experiments, in particular for track reconstruction algorithms. In terms of software, the increased combinatorial complexity will have to harnessed without any increase in budget. To engage the Computer Science community to contribute new ideas, we organized a Tracking Machine Learning challenge (TrackML) running on the Kaggle platform from March to June 2018, building on the experience of the successful Higgs Machine Learning challenge in 2014. The data were generated using [ACTS], an open source accurate tracking simulator, featuring a typical all silicon LHC tracking detector, with 10 layers of cylinders and disks. Simulated physics events (Pythia ttbar) overlaid with 200 additional collisions yield typically 10000 tracks (100000 hits) per event. The first lessons from the Accuracy phase of the challenge will be discussed.

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© 2018 IEEE.

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93829
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CaltechAUTHORS:20190314-135749360

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2019-03-14
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2021-11-16
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