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Haijun Yang

Researcher at Shanghai Jiao Tong University

Publications -  416
Citations -  39150

Haijun Yang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 100, co-authored 403 publications receiving 35114 citations. Previous affiliations of Haijun Yang include Yale University & Dresden University of Technology.

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The ATLAS Simulation Infrastructure

Georges Aad, +2585 more
- 25 Sep 2010 - 
TL;DR: The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid, including supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors.
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Elliptic Flow of Charged Particles in Pb-Pb Collisions at root s(NN)=2.76 TeV

K. Aamodt, +1014 more
- 13 Dec 2010 - 
TL;DR: In this paper, the first measurement of charged particle elliptic flow in Pb-Pb collisions at root s(NN) p = 2.76 TeV with the ALICE detector at the CERN Large Hadron Collider was performed in the central pseudorapidity region.
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Combined search for the Standard Model Higgs boson using up to 4.9 fb-1 of pp collision data at s=7TeV with the ATLAS detector at the LHC

Georges Aad, +3084 more
- 29 Mar 2012 - 
TL;DR: A combined search for the Standard Model Higgs boson with the ATLAS experiment at the LHC using datasets corresponding to integrated luminosities from 1.04 fb(-1) to 4.9 fb(1) of pp collisions is described in this paper.
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Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at root s(NN)=2.76 TeV

K. Aamodt, +941 more
- 20 Jan 2011 - 
TL;DR: In this paper, the centrality dependence of the chargedparticle multiplicity density at midrapidity in Pb-Pb collisions at root s(NN) = 2: 76 TeV is presented.
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Boosted decision trees as an alternative to artificial neural networks for particle identification

Byron P. Roe, +5 more
- 11 May 2005 - 
TL;DR: The efficacy of particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment, and it is expected that boosting algorithms will find wide application in physics.