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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
Morad Aaboud, Georges Aad1, Brad Abbott2, Jalal Abdallah3  +2843 moreInstitutions (197)
TL;DR: The algorithm removes calorimeter energy deposits due to charged hadrons from consideration during jet reconstruction, instead using measurements of their momenta from the inner tracker, which improves the accuracy of the charged-hadron measurement, while retaining the calorimeters' measurements of neutral-particle energies.
Abstract: This paper describes the implementation and performance of a particle flow algorithm applied to 20.2 fb(-1) of ATLAS data from 8 TeV proton-proton collisions in Run 1 of the LHC. The algorithm remo ...

125 citations

Journal ArticleDOI
TL;DR: The existence of top-quark partners with masses below 800 GeV is excluded at a 95% confidence level, and the first limit on these particles from the LHC is significantly more restrictive than previous limits.
Abstract: A search for the production of heavy partners of the top quark with charge 5/3 is performed in events with a pair of same-sign leptons. The data sample corresponds to an integrated luminosity of 19.5 fb^(−1) and was collected at s√=8 TeV by the CMS experiment. No significant excess is observed in the data above the expected background, and the existence of top-quark partners with masses below 800 GeV is excluded at a 95% confidence level, assuming they decay exclusively to tW. This is the first limit on these particles from the LHC, and it is significantly more restrictive than previous limits.

124 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, Ovsat Abdinov4  +2873 moreInstitutions (191)
TL;DR: In this paper, the authors present a survey of NSF projects in the European Union and the United States of America, including the following countries: Austria, Australia, Germany, Austria, Belgium, Canada, Czech Republic, Denmark, Netherlands, Norway, Portugal, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States, Netherlands and Switzerland.
Abstract: ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWF, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIEN-CIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; Lundbeck Foundation, Denmark; EPLANET; ERC; NSRF; European Union; IN2P3-CNRS, France; CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, Germany; DFG, Germany; HGF, Germany; MPG, Germany; AvH Foundation, Germany; GSRT, Greece; NSRF, Greece; ISF, Israel; MINERVA, Israel; GIF, Israel; DIP, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; FOM, Netherlands; NWO, Netherlands; BRF, Norway; RCN, Norway; MNiSW, Poland; GRICES, Portugal; FCT, Portugal; MERYS (MECTS), Romania; MES of Russia; ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SER, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, United Kingdom; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; DOE, United States of America; NSF, United States of America

123 citations

Journal ArticleDOI
TL;DR: In this paper, evidence for the associated production of a single top quark and W boson in pp collisions at √s=7 TeV with the CMS experiment at the LHC was presented.
Abstract: Evidence is presented for the associated production of a single top quark and W boson in pp collisions at √s=7 TeV with the CMS experiment at the LHC. The analyzed data correspond to an integrated luminosity of 4.9 fb^(-1). The measurement is performed using events with two leptons and a jet originated from a b quark. A multivariate analysis based on kinematic properties is utilized to separate the tt background from the signal. The observed signal has a significance of 4.0σ and corresponds to a cross section of 16_(-4)^(+5) pb, in agreement with the standard model expectation of 15.6±0.4_(-1.2)^(+1.0) pb.

123 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, A. A. Abdelalim4  +2923 moreInstitutions (184)
TL;DR: In this article, an overview of the ATLAS liquid argon calorimeter performance measured in situ with random triggers, calibration data, cosmic muons, and LHC beam splash events is presented.
Abstract: The ATLAS liquid argon calorimeter has been operating continuously since August 2006. At this time, only part of the calorimeter was readout, but since the beginning of 2008, all calorimeter cells have been connected to the ATLAS readout system in preparation for LHC collisions. This paper gives an overview of the liquid argon calorimeter performance measured in situ with random triggers, calibration data, cosmic muons, and LHC beam splash events. Results on the detector operation, timing performance, electronics noise, and gain stability are presented. High energy deposits from radiative cosmic muons and beam splash events allow to check the intrinsic constant term of the energy resolution. The uniformity of the electromagnetic barrel calorimeter response along eta (averaged over phi) is measured at the percent level using minimum ionizing cosmic muons. Finally, studies of electromagnetic showers from radiative muons have been used to cross-check the Monte Carlo simulation. The performance results obtained using the ATLAS readout, data acquisition, and reconstruction software indicate that the liquid argon calorimeter is well-prepared for collisions at the dawn of the LHC era.

123 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