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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, Jalal Abdallah3, Ovsat Abdinov4  +2853 moreInstitutions (190)
TL;DR: In this paper, the results from two previously published ATLAS analyses are interpreted in terms of third-generation leptoquarks decaying to [formula: see text] and [Formula] final states.
Abstract: Searches for pair-produced scalar leptoquarks are performed using 20 fb[Formula: see text] of proton-proton collision data provided by the LHC and recorded by the ATLAS detector at [Formula: see text] TeV. Events with two electrons (muons) and two or more jets in the final state are used to search for first (second)-generation leptoquarks. The results from two previously published ATLAS analyses are interpreted in terms of third-generation leptoquarks decaying to [Formula: see text] and [Formula: see text] final states. No statistically significant excess above the Standard Model expectation is observed in any channel and scalar leptoquarks are excluded at 95 % CL with masses up to [Formula: see text] 1050 GeV for first-generation leptoquarks, [Formula: see text] 1000 GeV for second-generation leptoquarks, [Formula: see text] 625 GeV for third-generation leptoquarks in the [Formula: see text] channel, and 200 [Formula: see text] 640 GeV in the [Formula: see text] channel.

91 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, J. Abdallah3, S. Abdel Khalek  +2901 moreInstitutions (199)
TL;DR: A blind analysis searching for the decay B-s(0) -> mu(+)mu(-) has been performed using proton-proton collisions at a centre-of-mass energy of 7 TeV recorded with the ATLAS detector at the LHC.

91 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, S. Abdel Khalek4  +2917 moreInstitutions (212)
TL;DR: In this paper, the results of a search for charged Higgs bosons decaying to a T lepton and a neutrino were presented based on 19.5 fb(-1) of proton-proton collision data at r...
Abstract: The results of a search for charged Higgs bosons decaying to a T lepton and a neutrino. H-+/- -> T-+/-nu, are presented. The analysis is based on 19.5 fb(-1) of proton-proton collision data at r ...

90 citations

Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Peter Davison3, Samuel Webb4  +2924 moreInstitutions (209)
TL;DR: The results of a search for supersymmetry in final states containing at least one isolated lepton (electron or muon), jets and large missing transverse momentum with the ATLAS detector at the Large Hadron Collider are reported in this paper.
Abstract: The results of a search for supersymmetry in final states containing at least one isolated lepton (electron or muon), jets and large missing transverse momentum with the ATLAS detector at the Large Hadron Collider are reported. The search is based on proton-proton collision data at a centre-of-mass energy s√=8 TeV collected in 2012, corresponding to an integrated luminosity of 20 fb−1. No significant excess above the Standard Model expectation is observed. Limits are set on supersymmetric particle masses for various supersymmetric models. Depending on the model, the search excludes gluino masses up to 1.32 TeV and squark masses up to 840 GeV. Limits are also set on the parameters of a minimal universal extra dimension model, excluding a compactification radius of 1/R c = 950 GeV for a cut-off scale times radius (ΛR c) of approximately 30.

90 citations

Journal ArticleDOI
Roel Aaij, C. Abellan Beteta1, Bernardo Adeva2, Marco Adinolfi3  +615 moreInstitutions (43)
TL;DR: In this paper, the authors studied the production of ϒ(1S), ϒ (2S) and ϒ-(3S) mesons in proton-proton collisions at the centre-of-mass energy of [Formula: see text] is studied with the LHCb detector.
Abstract: The production of ϒ(1S), ϒ(2S) and ϒ(3S) mesons in proton-proton collisions at the centre-of-mass energy of [Formula: see text] is studied with the LHCb detector. The analysis is based on a data sample of 25 pb-1 collected at the Large Hadron Collider. The ϒ mesons are reconstructed in the decay mode ϒ→μ+μ- and the signal yields are extracted from a fit to the μ+μ- invariant mass distributions. The differential production cross-sections times dimuon branching fractions are measured as a function of the ϒ transverse momentum pT and rapidity y, over the range pT <15 GeV/c and 2.0

89 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