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 heavy bottom-like quarks in 4.9 fb-1 of pp collisions at √s = 7 TeV

Abstract: Results are presented from a search for heavy bottom-like quarks, pair-produced in pp collisions at sqrt(s) = 7 TeV, undertaken with the CMS experiment at the LHC. The b' quarks are assumed to decay exclusively to tW. The b' anti-b' to t W(+) anti-t W(-) process can be identified by its distinctive signatures of three leptons or two leptons of same charge, and at least one b-quark jet. Using a data sample corresponding to an integrated luminosity of 4.9 inverse femtobarns, observed events are compared to the standard model background predictions, and the existence of b' quarks having masses below 611 GeV is excluded at 95% confidence level.

Measurement of the tt̄ production cross section in pp collisions at 7 TeV in lepton+jets events using b-quark jet identification

Abstract: A new measurement of the inclusive production cross section for pp to t t-bar is performed at a center-of-mass energy of 7 TeV using data collected by the CMS experiment at the LHC. The analysis uses a data sample corresponding to an integrated luminosity of 36 inverse picobarns, and is based on the final state with one isolated, high transverse momentum muon or electron, missing transverse energy, and hadronic jets. The ttbar content of the selected events is enhanced by requiring the presence of at least one jet consistent with b-quark hadronization. The measured cross section is 150 +/- 9 (stat.) +/- 17 syst.) +/- 6 (lumi.) pb and is in agreement with higher-order QCD calculations. The combination of this measurement with a previous CMS result based on dileptons gives 154 +/- 17 (stat.+syst.) +/- 6 (lumi.) pb.

Search for resonances decaying into top-quark pairs using fully hadronic decays in pp collisions with ATLAS at √s=7 TeV

Abstract: A search for resonances produced in 7 TeV proton-proton collisions and decaying into top-quark pairs is described. In this Letter events where the top-quark decay produces two massive jets with large transverse momenta recorded with the ATLAS detector at the Large Hadron Collider are considered. Two techniques that rely on jet substructure are used to separate top-quark jets from those arising from light quarks and gluons. In addition, each massive jet is required to have evidence of an associated bottom-quark decay. The data are consistent with the Standard Model, and limits can be set on the production cross section times branching fraction of a Z′ boson and a Kaluza-Klein gluon resonance. These limits exclude, at the 95% credibility level, Z′ bosons with masses 0.70-1.00 TeV as well as 1.28-1.32 TeV and Kaluza-Klein gluons with masses 0.70-1.62 TeV.

Measurement of the B ± production cross-section in pp collisions at root s = 7 TeV

Abstract: Measurements of the total and differential cross sections with respect to transverse momentum and rapidity for B+ mesons produced in pp collisions at sqrt(s) = 7 TeV are presented. The data correspond to an integrated luminosity of 5.8 inverse picobarns collected by the CMS experiment operating at the LHC. The exclusive decay B+ to J/psi K+, with the J/psi decaying to an oppositely charged muon pair, is used to detect B+ mesons and to measure the production cross section as a function of the transverse momentum and rapidity of the B. The total cross section for p_t(B) > 5 GeV and |y(B)| < 2.4 is measured to be 28.1 +/- 2.4 +/- 2.0 +/- 3.1 microbarns, where the first uncertainty is statistical, the second is systematic, and the last is from the luminosity measurement.

Search for dark matter in events with heavy quarks and missing transverse momentum in $pp$ collisions with the ATLAS detector

Abstract: This article reports on a search for dark matter pair production in association with bottom or top quarks in 20.3 fb−1 of pp collisions collected at s√=8 TeV by the ATLAS detector at the LHC. Events with large missing transverse momentum are selected when produced in association with high-momentum jets of which one or more are identified as jets containing b-quarks. Final states with top quarks are selected by requiring a high jet multiplicity and in some cases a single lepton. The data are found to be consistent with the Standard Model expectations and limits are set on the mass scale of effective field theories that describe scalar and tensor interactions between dark matter and Standard Model particles. Limits on the dark-matter--nucleon cross-section for spin-independent and spin-dependent interactions are also provided. These limits are particularly strong for low-mass dark matter. Using a simplified model, constraints are set on the mass of dark matter and of a coloured mediator suitable to explain a possible signal of annihilating dark matter.

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.).