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.

Search for Higgs and Z boson decays to J/ψγ and ϒ(nS)γ with the ATLAS detector

Abstract: A search for the decays of the Higgs and Z bosons to J/psi gamma and Upsilon(nS)gamma (n = 1,2,3) is performed with pp collision data samples corresponding to integrated luminosities of up to 20.3 fb(-1) collected at root s = 8 TeV with the ATLAS detector at the CERN Large Hadron Collider. No significant excess of events is observed above expected backgrounds and 95% C.L. upper limits are placed on the branching fractions. In the J/psi gamma final state the limits are 1.5 x 10(-3) and 2.6 x 10(-6) for the Higgs and Z boson decays, respectively, while in the Upsilon(1S, 2S, 3S)gamma. final states the limits are (1.3, 1.9, 1.3) x 10(-3) and (3.4, 6.5, 5.4) x 10(-6), respectively.

Measurement of ψ(2S) polarisation in pp collisions at √s = 7 TeV

Abstract: The polarisation of prompt mesons is measured by performing an angular analysis of decays using proton-proton collision data, corresponding to an integrated luminosity of 1.0, collected by the LHCb detector at a centre-of-mass energy of 7 TeV. The polarisation is measured in bins of transverse momentum and rapidity in the kinematic region and , and is compared to theoretical models. No significant polarisation is observed.

Search for a Higgs boson in the decay channel H -> ZZ((*)) -> q(q)over-barl(-)l(+) in pp collisions at root s=7 TeV

Abstract: A search for the standard model Higgs boson decaying into two Z bosons with subsequent decay into a final state containing two quark jets and two leptons, H to ZZ(*) to q q-bar l-l+ is presented. Results are based on data corresponding to an integrated luminosity of 4.6 inverse femtobarns of proton-proton collisions at sqrt(s)=7 TeV, collected with the CMS detector at the LHC. In order to discriminate between signal and background events, kinematic and topological quantities, including the angular spin correlations of the decay products, are employed. Events are further classified according to the probability of the jets to originate from quarks of light or heavy flavor or from gluons. No evidence for the Higgs boson is found, and upper limits on its production cross section are determined for a Higgs boson of mass between 130 and 600 GeV.

Measurement of theB0Production Cross Section inppCollisions ats=7TeV

Abstract: Measurements of the differential production cross sections $d\ensuremath{\sigma}/d{p}_{T}^{B}$ and $d\ensuremath{\sigma}/d{y}^{B}$ for ${B}^{0}$ mesons produced in $pp$ collisions at $\sqrt{s}=7\text{ }\text{ }\mathrm{TeV}$ are presented. The data set used was collected by the CMS experiment at the LHC and corresponds to an integrated luminosity of $40\text{ }\text{ }{\mathrm{pb}}^{\ensuremath{-}1}$. The production cross section is measured from ${B}^{0}$ meson decays reconstructed in the exclusive final state $J/\ensuremath{\psi}{K}_{S}^{0}$, with the subsequent decays $J/\ensuremath{\psi}\ensuremath{\rightarrow}{\ensuremath{\mu}}^{+}{\ensuremath{\mu}}^{\ensuremath{-}}$ and ${K}_{S}^{0}\ensuremath{\rightarrow}{\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}$. The total cross section for ${p}_{T}^{B}g5\text{ }\text{ }\mathrm{GeV}$ and $|{y}^{B}|l2.2$ is measured to be $33.2\ifmmode\pm\else\textpm\fi{}2.5\ifmmode\pm\else\textpm\fi{}3.5\text{ }\text{ }\ensuremath{\mu}\mathrm{b}$, where the first uncertainty is statistical and the second is systematic.

I and i

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 …

The Design and Analysis of Experiments

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

Machine learning

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.).