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.

Measurement of the production cross section for Z/γ* in association with jets in pp collisions at √s=7TeV with the ATLAS detector

Abstract: Results are presented on the production of jets of particles in association with a Z/gamma* boson, in proton-proton collisions at root s = 7 TeV with the ATLAS detector. The analysis includes the full 2010 data set, collected with a low rate of multiple proton-proton collisions in the accelerator, corresponding to an integrated luminosity of 36 pb(-1). Inclusive jet cross sections in Z/gamma* events, with Z/gamma* decaying into electron or muon pairs, are measured for jets with transverse momentum p(T) > 30 GeV and jet rapidity vertical bar y vertical bar < 4.4. The measurements are compared to next-to-leading-order perturbative QCD calculations, and to predictions from different Monte Carlo generators implementing leading-order matrix elements supplemented by parton showers.

Measurement of electroweak production of two jets in association with a Z boson in proton–proton collisions at s=8TeV

Abstract: The purely electroweak (EW) cross section for the production of two jets in association with a Z boson, in proton-proton collisions at sqrt(s) = 8 TeV, is measured using data recorded by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 19.7 inverse femtobarns. The electroweak cross section for the lljj final state (with l = e or mu and j representing the quarks produced in the hard interaction) in the kinematic region defined by M[ll] > 50 GeV, M[jj] > 120 GeV, transverse momentum pt[j] > 25 GeV, and pseudorapidity abs(eta[j]) < 5, is found to be sigma[EW](lljj) = 174 +/- 15 (stat) +/- 40 (syst) fb, in agreement with the standard model prediction. The associated jet activity of the selected events is studied, in particular in a signal-enriched region of phase space, and the measurements are found to be in agreement with QCD predictions.

Measurement of D0-D̄0 Mixing from a Time-Dependent Amplitude Analysis of D0→K+π-π0 Decays

Abstract: The authors present evidence of D{sup 0}-{bar D}{sup 0} mixing using a time-dependent amplitude analysis of the decay D{sup 0} {yields} K{sup +}{pi}{sup -}{pi}{sup 0} in a data sample of 384 fb{sup -1} collected with the BABAR detector at the PEP-II e{sup +}e{sup -} collider at SLAC. Assuming CP conservation, they measure the mixing parameters x{prime}{sub K{pi}{pi}{sup 0}} = [2.61{sub -0.68}{sup +0.57}(stat.) {+-} 0.39(syst.)]%, y{prime}{sub K{pi}{pi}{sup 0}} = [-0.06{sub -0.64}{sup +0.55}(stat.) {+-} 0.34(syst.)]%. The confidence level for the data to be consistent with the no-mixing hypothesis is 0.1%, including systematic uncertainties. This result is inconsistent with the no-mixing hypothesis with a significance of 3.2 standard deviations. They find no evidence of CP violation in mixing.

Search for the Higgs Boson in the H→WW(*)→l+νl–ν¯ decay channel in pp collisions at s=7TeV with the ATLAS detector

Abstract: A search for the Higgs boson has been performed in the H -> WW(*()) -> l+vl-(v) over bar channel (l = e/mu) with an integrated luminosity of 2.05 fb(-1) of pp collisions at root s = 7 TeV collected with the ATLAS detector at the Large Hadron Collider. No significant excess of events over the expected background is observed and limits on the Higgs boson production cross section are derived for a Higgs boson mass in the range 110 GeV< m(H) < 300 GeV. The observations exclude the presence of a standard model Higgs boson with a mass 145 < m(H) < 206 GeV at 95% confidence level.

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.).