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Christophe Royon

Researcher at University of Kansas

Publications -  1628
Citations -  97779

Christophe Royon is an academic researcher from University of Kansas. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 134, co-authored 1453 publications receiving 90249 citations. Previous affiliations of Christophe Royon include DSM & Humboldt University of Berlin.

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Journal ArticleDOI

Hard scattering in γp interactions

T. Ahmed, +373 more
- 24 Dec 1992 - 
TL;DR: In this paper, the final state in interactions of quasi-real photons with protons was investigated with the H1 detector at the HERA ep collider and the data can best be described by inclusion of resolved photon processess as predicted by QCD.
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Searching for axion-like particles with proton tagging at the LHC

TL;DR: In this paper, the expected bounds on the axion-photon coupling for a wide range of masses are estimated. But the expected bound is not applicable to the CP-even scalar, and the efficiency of the search is independent of the width of the ALP.
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Search for a Charged Higgs Boson Produced in the Vector-boson Fusion Mode with Decay H ± →W ± Z using pp Collisions at √s=8 TeV with the ATLAS Experiment

Georges Aad, +2811 more
TL;DR: A search for a charged Higgs boson, H(±), decaying to a W(±) boson and a Z boson is presented and the limits are compared with predictions from the Georgi-Machacek Higgs triplet model.
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Search for singly and pair-produced leptoquarks coupling to third-generation fermions in proton-proton collisions at s=13 TeV

Albert M. Sirunyan, +2356 more
- 10 Aug 2021 - 
TL;DR: In this article, a search for leptoquarks produced singly and in pairs in proton-proton collisions is presented, where the lepton is considered to be a scalar particle of charge −1/3e coupling to a top quark plus a tau lepton ( t τ ) or a bottom quark including a neutrino ( b ν ), or a vector particle of a charge +2/3 e coupling to t ν or b τ.
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A neural network clustering algorithm for the ATLAS silicon pixel detector

Georges Aad, +2911 more
TL;DR: A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented, reducing the number of clusters shared between tracks in highly energetic jets by up to a factor of three.