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.

Study of exclusive two-photon production of W+W- in pp collisions at √s=7 TeV and constraints on anomalous quartic gauge couplings

Abstract: A search for exclusive or quasi-exclusive W(+)W(-) production by photon-photon interactions, pp to p(*)W(+)W(-)p(*), at sqrt(s) = 7 TeV is reported using data collected by the CMS detector with an integrated luminosity of 5.05 inverse femtobarns. Events are selected by requiring a mu(+/-)e(-/+) vertex with no additional associated charged tracks and dilepton transverse momentum pt(mu(+/-)e(-/+)) > 30 GeV. Two events passing all selection requirements are observed in the data, compared to a standard model expectation of 2.2 +/- 0.4 signal events with 0.84 +/- 0.15 background. The tail of the dilepton pt distribution is studied for deviations from the standard model. No events are observed with pt > 100 GeV. Model-independent upper limits are computed and compared to predictions involving anomalous quartic gauge couplings. The limits on the parameters a[W,(0,C)]/Lambda^2 with a dipole form factor and an energy cutoff Lambda(cutoff) = 500 GeV are of the order of 10E-4.

Studies of jet mass in dijet and W/Z + jet events

Abstract: Invariant mass spectra for jets reconstructed using the anti-kt and Cambridge-Aachen algorithms are studied for different jet "grooming" techniques in data corresponding to an integrated luminosity of 5 inverse femtobarns, recorded with the CMS detector in proton-proton collisions at the LHC at a center-of-mass energy of 7 TeV. Leading-order QCD predictions for inclusive dijet and W/Z+jet production combined with parton-shower Monte Carlo models are found to agree overall with the data, and the agreement improves with the implementation of jet grooming methods used to distinguish merged jets of large transverse momentum from softer QCD gluon radiation.

Study of (W/Z)H production and Higgs boson couplings using H→ W W∗ decays with the ATLAS detector

Abstract: A search for Higgs boson production in association with a W or Z boson, in the H -> WW* decay channel, is performed with a data sample collected with the ATLAS detector at the LHC in proton-proton collisions at centre-of-mass energies root s = 7 TeV and 8TeV, corresponding to integrated luminosities of 4.5 fb(-1) and 20.3 fb(-1), respectively. The W H production mode is studied in two-lepton and three-lepton final states, while twolepton and four-lepton final states are used to search for the ZH production mode. The observed significance, for the combined WH and ZH production, is 2.5 standard deviations while a significance of 0.9 standard deviations is expected in the Standard Model Higgs boson hypothesis. The ratio of the combined W H and Z H signal yield to the Standard Model expectation, mu(VH), is found to be mu(VH) = 3.0(-1.1)(+1.3)(stat.)(-0.7)(+1.0) (sys.) for the Higgs boson mass of 125.36 GeV. The WH and ZH production modes are also combined with the gluon fusion and vector boson fusion production modes studied in the H -> WW* -> l nu l nu decay channel, resulting in an overall observed significance of 6.5 standard deviations and mu F-gg+VBF+VH = 1.16(-0.15)(+0.16)(stat.)(-0.15)(+0.18)(sys.). The results are interpreted in terms of scaling factors of the Higgs boson couplings to vector bosons (kappa(V)) and fermions (kappa(F)); the combined results are: vertical bar kappa(V)vertical bar = 1.06(-0.10)(+0.10), vertical bar kappa(F)vertical bar = 0.85(-0.20)(+0.26)

Measurement of forward W and Z boson production in pp collisions at √s=8 TeV

Abstract: Measurements are presented of electroweak boson production using data from pp collisions at a centre-of-mass energy of root s = 8TeV. The analysis is based on an integrated luminosity of 2.0 fb(-1) recorded with the LHCb detector. The bosons are identified in the W -> mu nu and Z -> mu(+)mu(-) decay channels. The cross-sections are measured for muons in the pseudorapidity range 2.0 20 GeV/c and, in the case of the Z boson, a dimuon mass within 60 mu(+)nu(-) = 1093.6 +/- 2.1 +/- 7.2 +/- 10.9 +/- 12.7 pb, sigma(W-) -> mu(-)nu(-) = 818.4 +/- 1.9 +/- 5.0 +/- 7.0 +/- 9.5 pb, sigma(Z) -> mu(+)mu(-) = 95.0 +/- 0.3 +/- 0.7 +/- 1.1 +/- 1.1 pb, where the first uncertainties are statistical, the second are systematic, the third are due to the knowledge of the LHC beam energy and the fourth are due to the luminosity determination. The evolution of the W and Z boson cross-sections with centre-of-mass energy is studied using previously reported measurements with 1.0 fb(-1) of data at 7 TeV. Differential distributions are also presented. Results are in good agreement with theoretical predictions at next-to-next-to-leading order in perturbative quantum chromodynamics.

Search for heavy resonances decaying into WW in the eνμν final state in pp collisions at s=13 TeV with the ATLAS detector.

Abstract: A search for neutral heavy resonances is performed in the WW -> e nu mu nu decay channel using pp collision data corresponding to an integrated luminosity of 36.1 fb(-1), collected at a centre-o ...

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.).