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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
(IOP Publishing, 2020-06)
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. ...
Pileup mitigation at CMS in 13 TeV data
(IOP Publishing, 2020-09)
With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting ...
Performance of reconstruction and identification of τ leptons decaying to hadrons and vτ in pp collisions at √s=13 TeV
(IOP Publishing, 2018-10)
The algorithm developed by the CMS Collaboration to reconstruct and identify tau leptons produced in proton-proton collisions at root s = 7 and 8 TeV, via their decays to hadrons and a neutrino, has been significantly ...
Reconstruction of signal amplitudes in the CMS electromagnetic calorimeter in the presence of overlapping proton-proton interactions
(IOP Publishing, 2020-10)
A template fitting technique for reconstructing the amplitude of signals produced by the lead tungstate crystals of the CMS electromagnetic calorimeter is described. This novel approach is designed to suppress the contribution ...
Performance of the CMS Level-1 trigger in proton-proton collisions at √s = 13 TeV
(2020-10)
At the start of Run 2 in 2015, the LHC delivered proton-proton collisions at a center-ofmass energy of 13 TeV. During Run 2 (years 2015-2018) the LHC eventually reached a luminosity of 2.1 x 10(34) cm(-2) s(-1), almost ...
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