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Martina Vit

Researcher at Ghent University

Publications -  430
Citations -  14572

Martina Vit is an academic researcher from Ghent University. The author has contributed to research in topics: Large Hadron Collider & Lepton. The author has an hindex of 49, co-authored 369 publications receiving 10387 citations. Previous affiliations of Martina Vit include University of Trento.

Papers
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Combined measurements of Higgs boson couplings in proton–proton collisions at √s=13Te

Albert M. Sirunyan, +2268 more
- 20 May 2019 - 
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.
Journal ArticleDOI

CMS Collaboration : XXVIIth International Conference on Ultrarelativistic Nucleus–NucleusCollisions (Quark Matter 2018)

Albert M. Sirunyan, +2271 more
- 01 Jan 2019 - 
Journal ArticleDOI

Search for invisible decays of a Higgs boson produced through vector boson fusion in proton-proton collisions at root s=13 TeV

Albert M. Sirunyan, +2301 more
- 10 Jun 2019 - 
TL;DR: In this article, a search for invisible decays of a Higgs boson via vector boson fusion is performed using proton-proton collision data collected with the CMS detector at the LHC in 2016 at a center-of-mass energy root s = 13 TeV, corresponding to an integrated luminosity of 35.9fb(-1).
Journal ArticleDOI

Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at s=13 TeV

Albert M. Sirunyan, +2358 more
- 19 Jun 2018 - 
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.
Journal ArticleDOI

Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements

Albert M. Sirunyan, +2251 more
- 03 Jan 2020 - 
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.