Particle-flow reconstruction and global event description with the CMS detector

By Cms Collaboration. 2017

1,251 citations recorded by the source corpus.

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/a2a2bca1c49b8e671d2a3fc4b79d06dde9061572

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In Nuclear and High Energy Physics

Citations in the corpus

Search for electroweak production of charginos and neutralinos in multilepton final states in proton-proton collisions at s=13$$ \sqrt{s}=13 $$ TeV (2017, methodology): “In the offline analysis, the information from all subdetectors is combined by the CMS particleflow algorithm [35].”
Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV (2018, methodology): “The PF algorithm refines the information from Tune-P, exploiting information from the full event, by selecting refits that significantly improve the balance of missing pT and by using a post-processing algorithm designed to preserve events that contain genuine missing energy [17].”
Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV (2017, background): “The global event reconstruction, also called particle-flow (PF) event reconstruction [5], consists of reconstructing and identifying each individual particle with an optimized combination of all subdetector information.”
Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report (2017, methodology): “In terms of experimental precision, the inclusive tt̄ production cross section has been measured by ATLAS and CMS Collaborations at √ s = 7, 8 and 13 TeV.”
Graph neural networks in particle physics (2020, background): “The particle flow — or energy flow — reconstruction is an algorithm that aims at assigning to a candidate particle all the measurements in each sub-detector [49].”