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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  +2847 moreInstitutions (207)
TL;DR: In this paper, a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb −¹ of proton-proton collision data collected by the ATLAS detector at the LHC at a center-of-mass energy √s = 8 TeV.
Abstract: This paper reports a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb −¹ of proton–proton collision data collected by the ATLAS detector at the LHC at a centre-of-mass energy √s = 8 TeV. A range of techniques for optimising the signal jet mass resolution are combined with various jet substructure variables. The results of these studies in Monte Carlo simulations show that a simple pairwise combination of groomed jet mass and one substructure variable can provide a 50 % efficiency for identifying W bosons with transverse momenta larger than 200 GeV while maintaining multijet background efficiencies of 2–4 % for jets with the same transverse momentum. These signal and background efficiencies are confirmed in data for a selection of tagging techniques.

78 citations

Journal ArticleDOI
TL;DR: In this paper, the authors measured the differential production cross sections in transverse momentum and rapidity for B0 mesons produced in pp collisions at square root(s) = 7 TeV.
Abstract: Measurements of the differential production cross sections in transverse momentum and rapidity for B0 mesons produced in pp collisions at sqrt(s) = 7 TeV are presented. The dataset used was collected by the CMS experiment at the LHC and corresponds to an integrated luminosity of 40 inverse picobarns. The production cross section is measured from B0 meson decays reconstructed in the exclusive final state J/Psi K-short, with the subsequent decays J/Psi to mu^+ mu^- and K-short to pi^+ pi^-. The total cross section for pt(B0) > 5 GeV and y(B0) < 2.2 is measured to be 33.2 +/- 2.5 +/- 3.5 microbarns, where the first uncertainty is statistical and the second is systematic.

78 citations

Journal ArticleDOI
Georges Aad1, Georges Aad2, E. Abat3, Brad Abbott4  +3253 moreInstitutions (185)
TL;DR: In this paper, the performance of the ATLAS detector in the first half a million minimum bias events of the LHC collision data was investigated at center-of-mass energies of 0.9 TeV and 2.36 TeV.
Abstract: More than half a million minimum-bias events of LHC collision data were collected by the ATLAS experiment in December 2009 at centre-of-mass energies of 0.9 TeV and 2.36 TeV. This paper reports on studies of the initial performance of the ATLAS detector from these data. Comparisons between data and Monte Carlo predictions are shown for distributions of several track- and calorimeter-based quantities. The good performance of the ATLAS detector in these first data gives confidence for successful running at higher energies.

78 citations

Journal ArticleDOI
TL;DR: Pseudorapidity (eta) distributions of charged particles produced in proton-proton collisions at a centre-of-mass energy of 8 TeV are measured in the ranges abs(eta) < 2.2 and 5.3 < abs(ta) < 6.4 covered by the CMS and TOTEM detectors, respectively as mentioned in this paper.
Abstract: Pseudorapidity (eta) distributions of charged particles produced in proton-proton collisions at a centre-of-mass energy of 8 TeV are measured in the ranges abs(eta) < 2.2 and 5.3 < abs(eta) < 6.4 covered by the CMS and TOTEM detectors, respectively. The data correspond to an integrated luminosity of 45 inverse microbarns. Measurements are presented for three event categories. The most inclusive category is sensitive to 91-96% of the total inelastic proton-proton cross section. The other two categories are disjoint subsets of the inclusive sample that are either enhanced or depleted in single diffractive dissociation events. The data are compared to models used to describe high-energy hadronic interactions. None of the models considered provide a consistent description of the measured distributions.

78 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, J. Abdallah3, A. A. Abdelalim4  +3049 moreInstitutions (193)
TL;DR: No excess of events is observed above the expected background and limits on the Higgs boson production times branching ratio to weakly interacting, long-lived particles are derived as a function of the particle proper decay length.
Abstract: A search for the decay of a light Higgs boson (120-140 GeV) to a pair of weakly interacting, long-lived particles in 1.94 fb(-1) of proton-proton collisions at root s = 7 TeV recorded in 2011 by the ATLAS detector is presented. The search strategy requires that both long-lived particles decay inside the muon spectrometer. No excess of events is observed above the expected background and limits on the Higgs boson production times branching ratio to weakly interacting, long-lived particles are derived as a function of the particle proper decay length.

78 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