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: In this article, a search for supersymmetry with R-parity conservation in proton-proton collisions at a centre-of-mass energy of 7 TeV is presented.

204 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, A. A. Abdelalim4  +2565 moreInstitutions (176)
TL;DR: An overview of the Tile Calorimeter performance as measured using random triggers, calibration data, data from cosmic ray muons and single beam data and the determination of the global energy scale was performed with an uncertainty of 4%.
Abstract: The Tile hadronic calorimeter of the ATLAS detector has undergone extensive testing in the experimental hall since its installation in late 2005. The readout, control and calibration systems have been fully operational since 2007 and the detector has successfully collected data from the LHC single beams in 2008 and first collisions in 2009. This paper gives an overview of the Tile Calorimeter performance as measured using random triggers, calibration data, data from cosmic ray muons and single beam data. The detector operation status, noise characteristics and performance of the calibration systems are presented, as well as the validation of the timing and energy calibration carried out with minimum ionising cosmic ray muons data. The calibration systems’ precision is well below the design value of 1%. The determination of the global energy scale was performed with an uncertainty of 4%.

203 citations

Journal ArticleDOI
Morad Aaboud, Alexander Kupco1, Peter Davison1, Samuel Webb1  +2900 moreInstitutions (67)
TL;DR: In this article, a search for new phenomena in final states with an energetic jet and large missing transverse momentum was performed using proton-proton collision data corresponding to an inte...
Abstract: Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses proton-proton collision data corresponding to an inte ...

203 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Ovsat Abdinov3  +2981 moreInstitutions (220)
TL;DR: In this article, a search was performed for resonant and non-resonant Higgs boson pair production in the $ \upgamma \ upgamma b\overline{b} $ final state.
Abstract: A search is performed for resonant and non-resonant Higgs boson pair production in the $ \upgamma \upgamma b\overline{b} $ final state. The data set used corresponds to an integrated luminosity of 36.1 fb$^{−1}$ of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No significant excess relative to the Standard Model expectation is observed. The observed limit on the non-resonant Higgs boson pair cross-section is 0.73 pb at 95% confidence level. This observed limit is equivalent to 22 times the predicted Standard Model cross-section. The Higgs boson self-coupling (κ$_{λ}$ = λ$_{HHH}$/λ$_{HHH}^{SM}$ ) is constrained at 95% confidence level to −8.2 < κ$_{λ}$ < 13.2. For resonant Higgs boson pair production through $ X\to HH\to \upgamma \upgamma b\overline{b} $ , the limit is presented, using the narrow-width approximation, as a function of m$_{X}$ in the range 260 GeV < m$_{X}$ < 1000 GeV. The observed limits range from 1.1 pb to 0.12 pb over this mass range.

202 citations

ReportDOI
01 Feb 2010
TL;DR: The International Large Detector (ILD) is a concept for a detector at the International Linear Collider, ILC as discussed by the authors, which will collide electrons and positrons at energies of initially 500 GeV, upgradeable to 1 TeV.
Abstract: The International Large Detector (ILD) is a concept for a detector at the International Linear Collider, ILC. The ILC will collide electrons and positrons at energies of initially 500 GeV, upgradeable to 1 TeV. The ILC has an ambitious physics program, which will extend and complement that of the Large Hadron Collider (LHC). A hallmark of physics at the ILC is precision. The clean initial state and the comparatively benign environment of a lepton collider are ideally suited to high precision measurements. To take full advantage of the physics potential of ILC places great demands on the detector performance. The design of ILD is driven by these requirements. Excellent calorimetry and tracking are combined to obtain the best possible overall event reconstruction, including the capability to reconstruct individual particles within jets for particle ow calorimetry. This requires excellent spatial resolution for all detector systems. A highly granular calorimeter system is combined with a central tracker which stresses redundancy and efficiency. In addition, efficient reconstruction of secondary vertices and excellent momentum resolution for charged particles are essential for an ILC detector. The interaction region of the ILC is designed to host two detectors, which can be moved into the beam position with a push-pull scheme. The mechanical design of ILD and the overall integration of subdetectors takes these operational conditions into account.

202 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