scispace - formally typeset
Search or ask a question
Author

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
More filters
Journal ArticleDOI
TL;DR: A search for pair-produced heavy vectorlike charge-2/3 quarks, T, in pp collisions at a center-of-mass energy of 7 TeV, is performed with the CMS detector at the LHC and the number of observed events is found to be consistent with the standard model background prediction.
Abstract: A search for pair-produced heavy vector-like charge-2/3 quarks, T, in pp collisions at a center-of-mass energy of 7 TeV, is performed with the CMS detector at the LHC. Events consistent with the flavor-changing-neutral-current decay of a T quark to a top quark and a Z boson are selected by requiring two leptons from the Z-boson decay, as well as an additional isolated charged lepton. In a data sample corresponding to an integrated luminosity of 1.14 inverse femtobarns, the number of observed events is found to be consistent with the standard model background prediction. Assuming a branching fraction of 100% for the decay T to tZ, a T quark with a mass less than 475 GeV/c^2 is excluded at the 95% confidence level.

67 citations

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, Jalal Abdallah4  +2919 moreInstitutions (200)
TL;DR: In this article, the differential cross section for the process Z/gamma* -> ll (l = e, mu) as a function of dilepton invariant mass is measured in pp collisions at root s = 7 TeV at the LHC using the ATLAS detector.
Abstract: The differential cross section for the process Z/gamma* -> ll (l = e, mu) as a function of dilepton invariant mass is measured in pp collisions at root s = 7 TeV at the LHC using the ATLAS detector The measurement is performed in the e and mu channels for invariant masses between 26 GeV and 66 GeV using an integrated luminosity of 16 fb(-1) collected in 2011 and these measurements are combined The analysis is extended to invariant masses as low as 12 GeV in the muon channel using 35 pb(-1) of data collected in 2010 The cross sections are determined within fiducial acceptance regions and corrections to extrapolate the measurements to the full kinematic range are provided Next-to-next-to-leading-order QCD predictions provide a significantly better description of the results than next-to-leading-order QCD calculations, unless the latter are matched to a parton shower calculation

67 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Dale Charles Abbott3, A. Abed Abud4  +2960 moreInstitutions (196)
TL;DR: In this paper, single and double-differential cross-section measurements for the production of top-quark pairs, in the lepton + jets channel at particle and parton level, are presented.
Abstract: Single- and double-differential cross-section measurements are presented for the production of top-quark pairs, in the lepton + jets channel at particle and parton level. Two topologies, resolved a ...

67 citations

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, J. Abdallah4  +2921 moreInstitutions (199)
TL;DR: In this paper, the sum of transverse energy of particles as a function of particle pseudorapidity was measured in proton-proton collisions at a center-of-mass energy, root s = 7 TeV using the ATLAS detector at the Large Hadron Collider.
Abstract: This paper describes measurements of the sum of the transverse energy of particles as a function of particle pseudorapidity, eta, in proton-proton collisions at a centre-of-mass energy, root s = 7 TeV using the ATLAS detector at the Large Hadron Collider. The measurements are performed in the region \eta\ < 4.8 for two event classes: those requiring the presence of particles with a low transverse momentum and those requiring particles with a significant transverse momentum. In the second dataset measurements are made in the region transverse to the hard scatter. The distributions are compared to the predictions of various Monte Carlo event generators, which generally tend to underestimate the amount of transverse energy at high \eta\.

67 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, J. Abdallah3, A. A. Abdelalim4  +3030 moreInstitutions (180)
TL;DR: In this paper, a search for supersymmetric particles in events with large missing transverse momentum and at least one heavy flavour jet candidate in sqrt{s} = 7 TeV proton-proton collisions is presented.

67 citations


Cited by
More filters
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