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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
Roel Aaij, C. Abellan Beteta1, C. Abellan Beteta2, Bernardo Adeva3  +619 moreInstitutions (55)
TL;DR: In this article, a study of mixing and indirect CP violation in D-0 mesons through the determination of the parameters y(CP) and A(Gamma) is presented.
Abstract: A study of mixing and indirect CP violation in D-0 mesons through the determination of the parameters y(CP) and A(Gamma) is presented. The parameter y(CP) is the deviation from unity of the ratio of effective lifetimes measured in D-0 decays to the CP eigenstate K+K- with respect to decays to the Cabibbo favoured mode K-pi(+). The result measured using data collected by LHCb in 2010, corresponding to an integrated luminosity of 29 pb(-1), is y(CP) = (5.5 +/- 6.3(stat) +/- 4.1(syst)) x 10(-3). The parameter A(Gamma) is the asymmetry of effective lifetimes measured in decays of D-0 and (D) over bar (0) mesons to K+K-. The result is A = (-5.9 +/- 5.9(stat) +/- 2.1(syst)) x 10(-3). A data-driven technique is used to correct for lifetime-biasing effects.

47 citations

Journal ArticleDOI
D. Buskulic1, I. De Bonis1, D. Decamp1, Philippe Ghez1  +396 moreInstitutions (27)
TL;DR: In this paper, two samples of exclusive semileptonic decays, 579 events and 261 events, were selected from approximately 3.9 million hadronic Z decays collected by the ALEPH detector at LEP.

46 citations

Journal ArticleDOI
Roel Aaij1, Bernardo Adeva2, Marco Adinolfi3, Ziad Ajaltouni4  +777 moreInstitutions (54)
TL;DR: In this article, a measurement of the phase difference between the short and long-distance contributions to the [Formula: see text] decay is performed by analysing the dimuon mass distribution.
Abstract: A measurement of the phase difference between the short- and long-distance contributions to the [Formula: see text] decay is performed by analysing the dimuon mass distribution. The analysis is based on pp collision data corresponding to an integrated luminosity of 3[Formula: see text] collected by the LHCb experiment in 2011 and 2012. The long-distance contribution to the [Formula: see text] decay is modelled as a sum of relativistic Breit-Wigner amplitudes representing different vector meson resonances decaying to muon pairs, each with their own magnitude and phase. The measured phases of the [Formula: see text] and [Formula: see text] resonances are such that the interference with the short-distance component in dimuon mass regions far from their pole masses is small. In addition, constraints are placed on the Wilson coefficients, [Formula: see text] and [Formula: see text], and the branching fraction of the short-distance component is measured.

46 citations

Journal ArticleDOI
TL;DR: In this article, a search for resonances decaying to top quark-antiquark pairs is performed using a dilepton+jets data sample recorded by the CMS experiment at the LHC in pp collisions at √s=7 TeV corresponding to an integrated luminosity of 5.0
Abstract: A search for resonances decaying to top quark-antiquark pairs is performed using a dilepton+jets data sample recorded by the CMS experiment at the LHC in pp collisions at √s=7 TeV corresponding to an integrated luminosity of 5.0 fb-1. No significant deviations from the standard model background are observed. Upper limits are presented for the production cross section times branching fraction of top quark-antiquark resonances for masses from 750 to 3000 GeV. In particular, the existence of a leptophobic topcolor particle Z′ is excluded at the 95% confidence level for resonance masses MZ′<1.3 TeV for ΓZ′=0.012MZ′, and M<1.9 TeV for ΓZ′=0.10MZ′.

46 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Ovsat Abdinov3  +2940 moreInstitutions (198)
TL;DR: The response of the ATLAS detector to large-radius jets is measured in situ using 36.2 fb(-1) of root s = 13TeV proton-proton collisions provided by the LHC and recorded by ATLAS experiment as discussed by the authors.
Abstract: The response of the ATLAS detector to large-radius jets is measured in situ using 36.2 fb(-1) of root s = 13TeV proton-proton collisions provided by the LHC and recorded by the ATLAS experiment dur ...

46 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