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Cristina Riccardi

Researcher at University of Pavia

Publications -  1785
Citations -  101760

Cristina Riccardi is an academic researcher from University of Pavia. The author has contributed to research in topics: Large Hadron Collider & Lepton. The author has an hindex of 129, co-authored 1627 publications receiving 91452 citations. Previous affiliations of Cristina Riccardi include Université libre de Bruxelles & University of Florence.

Papers
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Measurement of the B± Meson Nuclear Modification Factor in Pb-Pb Collisions at √sNN=5.02 TeV

Albert M. Sirunyan, +2175 more
- 13 Oct 2017 - 
TL;DR: In this article, the authors measured the differential production cross sections of B-+/- mesons via the exclusive decay channels B−/- -> J/psi K-+/- -> mu(+)mu K--(+/-) as a function of transverse momentum in pp and Pb-Pb collisions at a center-of-mass energy root s(NN) = 5.02 TeV per nucleon pair with the CMS detector at the LHC.
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Measurement of prompt J/ψ pair production in pp collisions at √s = 7 Tev

Vardan Khachatryan, +2196 more
- 17 Sep 2014 - 
TL;DR: In this article, the high transverse-momentum region of J/psi meson pair production is measured in a phase space defined by the individual J/Psi transverse momentum and rapidity.
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Measurement of the Splitting Function in pp and Pb-Pb Collisions at sNN =5.02 TeV

Albert M. Sirunyan, +2235 more
- 03 Apr 2018 - 
TL;DR: The momentum ratio of the two leading partons, resolved as subjets, provides information about the parton shower evolution and indicates a more unbalanced momentum ratio in central PbPb and pp collisions.
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Search for resonant and nonresonant new phenomena in high-mass dilepton final states at √s = 13 TeV

Albert M. Sirunyan, +2407 more
- 27 Jul 2021 - 
TL;DR: In this paper, a data set of proton-proton collisions collected by the CMS experiment at the LHC at s = 13 TeV from 2016 to 2018 corresponding to a total integrated luminosity of up to 140 fb−1 is analyzed.
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Identification of Heavy, Energetic, Hadronically Decaying Particles Using Machine-Learning Techniques

Albert M. Sirunyan, +2305 more
- 03 Jun 2020 - 
TL;DR: In this article, machine learning techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks.