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Francesco Fienga

Researcher at University of Naples Federico II

Publications -  244
Citations -  11411

Francesco Fienga is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 48, co-authored 244 publications receiving 8901 citations. Previous affiliations of Francesco Fienga include Université libre de Bruxelles & University of Basilicata.

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

Albert M. Sirunyan, +2215 more
TL;DR: A fully-fledged particle-flow reconstruction algorithm tuned to the CMS detector was developed and has been consistently used in physics analyses for the first time at a hadron collider as mentioned in this paper.
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Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Albert M. Sirunyan, +2241 more
TL;DR: In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented.
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Combined measurements of Higgs boson couplings in proton–proton collisions at √s=13Te

Albert M. Sirunyan, +2268 more
TL;DR: Combined measurements of the production and decay rates of the Higgs boson, as well as its couplings to vector bosons and fermions, are presented and constraints are placed on various two Higgs doublet models.
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Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at s=13 TeV

Albert M. Sirunyan, +2358 more
TL;DR: In this paper, the performance of the modified system is studied using proton-proton collision data at center-of-mass energy √s=13 TeV, collected at the LHC in 2015 and 2016.
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Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements

Albert M. Sirunyan, +2251 more
TL;DR: For the first time, predictions from pythia8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.