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 charged Higgs bosons through the violation of lepton universality in tt events using pp collision data at √S=7 TeV with the ATLAS experiment

Abstract: In several extensions of the Standard Model, the top quark can decay into a bottom quark and a light charged Higgs boson H+, t -> bH(+), in addition to the Standard Model decay t -> bW. Since W bosons decay to the three lepton generations equally, while H+ may predominantly decay into tau nu, charged Higgs bosons can be searched for using the violation of lepton universality in top quark decays. The analysis in this paper is based on 4.6 fb(-1) of proton-proton collision data at root s = 7 TeV collected by the ATLAS experiment at the Large Hadron Collider. Signatures containing leptons (e or mu) and/or a hadronically decaying tau (tau(had)) are used. Event yield ratios between e+ tau(had) and e + mu, as well as between mu + tau(had) and mu + e, final states are measured in the data and compared to predictions from simulations. This ratio-based method reduces the impact of systematic uncertainties in the analysis. No significant deviation from the Standard Model predictions is observed. With the assumption that the branching fraction B(H+ -> tau nu) is 100%, upper limits in the range 3.2%-4.4% can be placed on the branching fraction B(t -> bH(+)) for charged Higgs boson masses m(H+) in the range 90-140GeV. After combination with results from a search for charged Higgs bosons in t (t) over bar decays using the tau(had) + jets final state, upper limits on B(t -> bH(+)) can be set in the range 0.8%-3.4%, for m(H+) in the range 90-160GeV.

Search for high-mass resonances decaying into τ-lepton pairs in pp collisions at s=7TeV

Abstract: A search for high-mass resonances decaying into tau-lepton pairs is performed using a data sample of pp collisions at sqrt(s)=7 TeV. The data were collected with the CMS detector at the LHC and correspond to an integrated luminosity of 4.9 inverse femtobarns. The number of observed events is in agreement with the standard model prediction. An upper limit on the product of the resonance cross section and branching fraction into tau-lepton pairs is calculated as a function of the resonance mass. Using the sequential standard model resonance Z'(SSM) and the superstring-inspired E(6) model with resonance Z'(psi) as benchmarks, resonances with standard model couplings with masses below 1.4 and 1.1 TeV, respectively, are excluded at 95% confidence level.

Search for W′→tb→qqbb decays in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: A search for a massive [Formula: see text] gauge boson decaying to a top quark and a bottom quark is performed with the ATLAS detector in [Formula: see text] collisions at the LHC. The dataset was taken at a centre-of-mass energy of [Formula: see text] and corresponds to [Formula: see text] of integrated luminosity. This analysis is done in the hadronic decay mode of the top quark, where novel jet substructure techniques are used to identify jets from high-momentum top quarks. This allows for a search for high-mass [Formula: see text] bosons in the range 1.5-3.0 [Formula: see text]. [Formula: see text]-tagging is used to identify jets originating from [Formula: see text]-quarks. The data are consistent with Standard Model background-only expectations, and upper limits at 95 % confidence level are set on the [Formula: see text] cross section times branching ratio ranging from [Formula: see text] to [Formula: see text] for left-handed [Formula: see text] bosons, and ranging from [Formula: see text] to [Formula: see text] for [Formula: see text] bosons with purely right-handed couplings. Upper limits at 95 % confidence level are set on the [Formula: see text]-boson coupling to [Formula: see text] as a function of the [Formula: see text] mass using an effective field theory approach, which is independent of details of particular models predicting a [Formula: see text] boson.

Measurement of the tt¯ production cross section in the τ+jets channel in pp collisions at s√=7 TeV

Abstract: The top-quark pair production cross section in 7 TeV center-of-mass energy proton–proton collisions is measured using data collected by the CMS detector at the LHC. The measurement uses events with one jet identified as a hadronically decaying τ lepton and at least four additional energetic jets, at least one of which is identified as coming from a b quark. The analyzed data sample corresponds to an integrated luminosity of 3.9 fb−1 recorded by a dedicated multijet plus hadronically decaying τ trigger. A neural network has been developed to separate the top-quark pairs from the W+jets and multijet backgrounds. The measured value of σtt¯=152±12(stat.)±32(syst.)±3(lum.) pb is consistent with the standard model predictions.

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.).