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Yang Gao

Bio: Yang Gao is an academic researcher from University of Surrey. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 168, co-authored 2047 publications receiving 146301 citations. Previous affiliations of Yang Gao include China Agricultural University & University of Kassel.


Papers
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Journal ArticleDOI
Georges Aad1, Brad Abbott2, J. Abdallah3, A. A. Abdelalim4  +3078 moreInstitutions (188)
TL;DR: In this article, a search for new interactions and resonances produced in LHC proton-proton (pp) collisions at a centre-of-mass energy root s = 7 TeV was performed with the ATLAS detector.
Abstract: A search for new interactions and resonances produced in LHC proton-proton (pp) collisions at a centre-of-mass energy root s = 7 TeV was performed with the ATLAS detector. Using a dataset with an integrated luminosity of 36 pb(-1), dijet mass and angular distributions were measured up to dijet masses of similar to 3.5 TeV and were found to be in good agreement with Standard Model predictions. This analysis sets limits at 95% CL on various models for new physics: an excited quark is excluded for mass between 0.60 and 2.64 TeV, an axigluon hypothesis is excluded for axigluon masses between 0.60 and 2.10 TeV and quantum black holes are excluded in models with six extra space-time dimensions for quantum gravity scales between 0.75 and 3.67 TeV. Production cross section limits as a function of dijet mass are set using a simplified Gaussian signal model to facilitate comparisons with other hypotheses. Analysis of the dijet angular distribution using a novel technique simultaneously employing the dijet mass excludes quark contact interactions with a compositeness scale 3 below 9.5 TeV.

142 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, J. Abdallah3, A. A. Abdelalim4  +3040 moreInstitutions (194)
TL;DR: In this paper, an update of a search for supersymmetry in final states containing jets, missing transverse momentum, and one isolated electron or muon is presented, using 1.04 fb(-1) of proton-proton collision data at root s =7 TeV recorded by the ATLAS experiment at the LHC in the first half of 2011.
Abstract: We present an update of a search for supersymmetry in final states containing jets, missing transverse momentum, and one isolated electron or muon, using 1.04 fb(-1) of proton-proton collision data at root s =7 TeV recorded by the ATLAS experiment at the LHC in the first half of 2011. The analysis is carried out in four distinct signal regions with either three or four jets and variations on the (missing) transverse momentum cuts, resulting in optimized limits for various supersymmetry models. No excess above the standard model background expectation is observed. Limits are set on the visible cross section of new physics within the kinematic requirements of the search. The results are interpreted as limits on the parameters of the minimal supergravity framework, limits on cross sections of simplified models with specific squark and gluino decay modes, and limits on parameters of a model with bilinear R-parity violation.

142 citations

Journal ArticleDOI
TL;DR: The anisotropy parameter (v2) of the particles is extracted by correlating charged tracks with respect to the event-plane reconstructed by using the energy deposited in forward-angle calorimeters, with the decline persisting up to at least pp(T)=40 GeV/c over the full centrality range measured.
Abstract: The azimuthal anisotropy of charged particles in PbPb collisions at nucleon-nucleon center-of-mass energy of 2.76 TeV is measured with the CMS detector at the LHC over an extended transverse momentum (pt) range up to approximately 60 GeV. The data cover both the low-pt region associated with hydrodynamic flow phenomena and the high-pt region where the anisotropies may reflect the path-length dependence of parton energy loss in the created medium. The anisotropy parameter (v2) of the particles is extracted by correlating charged tracks with respect to the event-plane reconstructed by using the energy deposited in forward-angle calorimeters. For the six bins of collision centrality studied, spanning the range of 0-60% most-central events, the observed v2 values are found to first increase with pt, reaching a maximum around pt = 3 GeV, and then to gradually decrease to almost zero, with the decline persisting up to at least pt = 40 GeV over the full centrality range measured.

142 citations

Sally Dawson, Seth Conrad Zenz, Tao Han, M. Kurata, Kumar Arun Kumar, Keisuke Fujii, Kirill Melnikov, K. Krueger, N. Solyak, Yue Zhang, J. E. Brau, A. Ajaib, Gudrid Moortgat-Pick, Shoichi Watanuki, J. Tian, M. A. Thomson, Gabe Shaughnessy, Kirill Prokofiev, J. O. Nielsen, Ilia Gogoladze, Eric Feng, R. Essig, J.L. Hewett, Jianming Qian, Patrick Janot, Christopher Samuel Pollard, A. Ishikawa, A. Miyamoto, C. Calancha-Paredes, Toshiki Tajima, Shufang Su, V. I. Telnov, Mayda Velasco, S. Kawada, M. De Gruttola, Taikan Suehara, Dirk Zerwas, Vincent Rodriguez, A. V. Kotwal, Y. Ilchenko, Kyle Cranmer, Tomohiko Tanabe, Elliot Lipeles, John Stupak, Ahmed Ismail, E. Brownson, C. Grojean, Michael Rauch, Joshua Sayre, Francis John Petriello, Christopher George Tully, A. Anastassov, Veronica Sanz, O. Bake, Yang Gao, W. Chou, Ulrich Heintz, Tom Rizzo, J. F. Strube, Georg Weiglein, H. Ono, Hideki Okawa, Ian M. Lewis, Koji Tsumura, Yang Li, Ricardo Gonçalo, S. Heinemeyer, Markus Klute, J. Olsen, N. Graf, T. Liu, Robert Clare, Marc Sher, Neeti Parashar, G. Kukartsev, Hiroshi Yokoya, Peter Onyisi, Howard E. Haber, Y. Zhou, Zhen Liu, Jenny List, Tim Barklow, T. Ma, Cheng Chen, Andrei Gritsan, Robert Kehoe, M. Cahill-Rowley, T. Robens, Frank Simon, Andrew Whitbeck, A. Blondel, V. Barger, Meenakshi Narain, T. M. Liss, Bruce Mellado, Richard B. Lipton, Stefania Gori, P. Roloff, Cooper Wagner, Qaisar Shafi, Rick J. Van Kooten, Kei Yagyu, N. Okada, Paul B. Mackenzie, Scott Thomas, D. M. Asner, Roni Harnik, M. Battaglia, I. Low, H. A. Neal, W-M. Yao, G. G. Hanson, Brian Batell, James S. Gainer, Lisa L. Everett, Vipul Jain, Cem Salih Ün, Nathaniel Craig, Song-Ming Wang, David B. Cline, Michael E. Peskin, Tilman Plehn, A. Elagin, L. Linssen, Joshua Kunkle, S. Bolognesi, G. Mourou, Heather E. Logan, Richard Ruiz, C. T. Potter, Stefan Guindon, S. Berge, Ian Anderson, Shinya Kanemura 
30 Oct 2013
TL;DR: The work of the Energy Frontier Higgs Boson working group of the 2013 Community Summer Study (Snowmass) as discussed by the authors summarizes the key elements of a precision Higgs physics program and document the physics potential of future experimental facilities as elucidated during the Snowmass study.
Abstract: This report summarizes the work of the Energy Frontier Higgs Boson working group of the 2013 Community Summer Study (Snowmass). We identify the key elements of a precision Higgs physics program and document the physics potential of future experimental facilities as elucidated during the Snowmass study. We study Higgs couplings to gauge boson and fermion pairs, double Higgs production for the Higgs self-coupling, its quantum numbers and $CP$-mixing in Higgs couplings, the Higgs mass and total width, and prospects for direct searches for additional Higgs bosons in extensions of the Standard Model. Our report includes projections of measurement capabilities from detailed studies of the Compact Linear Collider (CLIC), a Gamma-Gamma Collider, the International Linear Collider (ILC), the Large Hadron Collider High-Luminosity Upgrade (HL-LHC), Very Large Hadron Colliders up to 100 TeV (VLHC), a Muon Collider, and a Triple-Large Electron Positron Collider (TLEP).

141 citations

Journal ArticleDOI
Morad Aaboud, Alexander Kupco1, Samuel Webb2, Timo Dreyer3  +2969 moreInstitutions (195)
TL;DR: Algorithms used for the reconstruction and identification of electrons in the central region of the ATLAS detector at the Large Hadron Collider (LHC) are presented in this article, these algorithms a...
Abstract: Algorithms used for the reconstruction and identification of electrons in the central region of the ATLAS detector at the Large Hadron Collider (LHC) are presented in this paper; these algorithms a ...

140 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
Claude Amsler1, Michael Doser2, Mario Antonelli, D. M. Asner3  +173 moreInstitutions (86)
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.

12,798 citations