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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, S. Abdel Khalek4  +2879 moreInstitutions (193)
TL;DR: In this article, a search for direct top squark pair production using events with at least two leptons including a same-flavour opposite-sign pair with invariant mass consistent with the boson mass, jets tagged as originating from -quarks and missing transverse momentum is performed with proton-proton collision data at collected with the ATLAS detector at the LHC in 2012.
Abstract: A search is presented for direct top squark pair production using events with at least two leptons including a same-flavour opposite-sign pair with invariant mass consistent with the boson mass, jets tagged as originating from -quarks and missing transverse momentum. The analysis is performed with proton-proton collision data at collected with the ATLAS detector at the LHC in 2012 corresponding to an integrated luminosity of 20.3 fb. No excess beyond the Standard Model expectation is observed. Interpretations of the results are provided in models based on the direct pair production of the heavier top squark state () followed by the decay to the lighter top squark state () via , and for pair production in natural gauge-mediated supersymmetry breaking scenarios where the neutralino () is the next-to-lightest supersymmetric particle and decays producing a boson and a gravitino () via the process.

71 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, A. A. Abdelalim4  +2658 moreInstitutions (165)
TL;DR: In this paper, the performance of the trigger and tracking chambers, their alignment, the detector control system, the data acquisition and the analysis programs are discussed. And the results show that the detector is close to the design performance and that the Muon Spectrometer is ready to detect muons produced in high energy protonproton collisions.
Abstract: The ATLAS detector at the Large Hadron Collider has collected several hundred million cosmic ray events during 2008 and 2009. These data were used to commission the Muon Spectrometer and to study the performance of the trigger and tracking chambers, their alignment, the detector control system, the data acquisition and the analysis programs. We present the performance in the relevant parameters that determine the quality of the muon measurement. We discuss the single element efficiency, resolution and noise rates, the calibration method of the detector response and of the alignment system, the track reconstruction efficiency and the momentum measurement. The results show that the detector is close to the design performance and that the Muon Spectrometer is ready to detect muons produced in high energy proton-proton collisions.

71 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Ovsat Abdinov3  +2915 moreInstitutions (213)
TL;DR: In this paper, the results of a search for gluinos in final states with an isolated electron or muon, multiple jets and large missing transverse momentum using proton-proton collision data at a centre-of-mass energy of 13 TeV are presented.
Abstract: The results of a search for gluinos in final states with an isolated electron or muon, multiple jets and large missing transverse momentum using proton--proton collision data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV are presented. The dataset used was recorded in 2015 by the ATLAS experiment at the Large Hadron Collider and corresponds to an integrated luminosity of 3.2 fb$^{-1}$. Six signal selections are defined that best exploit the signal characteristics. The data agree with the Standard Model background expectation in all six signal selections, and the largest deviation is a 2.1 standard deviation excess. The results are interpreted in a simplified model where pair-produced gluinos decay via the lightest chargino to the lightest neutralino. In this model, gluinos are excluded up to masses of approximately 1.6 TeV depending on the mass spectrum of the simplified model, thus surpassing the limits of previous searches.

71 citations

Journal ArticleDOI
TL;DR: In this paper, the electron charge asymmetry is measured in bins of the absolute value of electron pseudorapidity in the range of $|\ensuremath{\eta}|l2.
Abstract: A measurement of the electron charge asymmetry in inclusive $pp\ensuremath{\rightarrow}W+X\ensuremath{\rightarrow}e\ensuremath{ u}+X$ production at $\sqrt{s}=7\text{ }\text{ }\mathrm{TeV}$ is presented based on data recorded by the CMS detector at the LHC and corresponding to an integrated luminosity of $840\text{ }\text{ }{\mathrm{pb}}^{\ensuremath{-}1}$. The electron charge asymmetry reflects the unequal production of ${W}^{+}$ and ${W}^{\ensuremath{-}}$ bosons in $pp$ collisions. The electron charge asymmetry is measured in bins of the absolute value of electron pseudorapidity in the range of $|\ensuremath{\eta}|l2.4$. The asymmetry rises from about 0.1 to 0.2 as a function of the pseudorapidity and is measured with a relative precision better than 7%. This measurement provides new stringent constraints for parton distribution functions.

71 citations

Journal ArticleDOI
TL;DR: In this article, the authors measured the t t-bar charge asymmetry as a function of rapidity, transverse momentum, and invariant mass of the t -bar system.

71 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