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 hadronic activity in events with a Z and two jets and extraction of the cross section for the electroweak production of a Z with two jets in pp collisions at √s = 7 TeV

Abstract: The first measurement of the electroweak production cross section of a Z boson with two jets (Zjj) in pp collisions at s√=7 TeV is presented, based on a data sample recorded by the CMS experiment at the LHC with an integrated luminosity of 5 fb^(−1). The cross section is measured for the lljj (l = e, μ) final state in the kinematic region m_(ll) > 50 GeV, m_(jj) > 120 GeV, transverse momenta p^j_T>25 GeV and pseudorapidity |η_j| < 4.0. The measurement, combining the muon and electron channels, yields σ = 154 ± 24 (stat.) ± 46 (exp. syst.) ± 27 (th. syst.) ± 3 (lum.) fb, in agreement with the theoretical cross section. The hadronic activity, in the rapidity interval between the jets, is also measured. These results establish an important foundation for the more general study of vector boson fusion processes, of relevance for Higgs boson searches and for measurements of electroweak gauge couplings and vector boson scattering.

Search for a new heavy gauge-boson resonance decaying into a lepton and missing transverse momentum in 36 fb - 1 of pp collisions at √s=13 TeV with the ATLAS experiment

Abstract: The results of a search for new heavy $$W^\prime $$ bosons decaying to an electron or muon and a neutrino using proton–proton collision data at a centre-of-mass energy of $$\sqrt{s}~=~13$$ TeV are presented. The dataset was collected in 2015 and 2016 by the ATLAS experiment at the Large Hadron Collider and corresponds to an integrated luminosity of 36.1 $$\text{ fb }^{-1}$$ . As no excess of events above the Standard Model prediction is observed, the results are used to set upper limits on the $$W^\prime $$ boson cross-section times branching ratio to an electron or muon and a neutrino as a function of the $$W^\prime $$ mass. Assuming a $$W^\prime $$ boson with the same couplings as the Standard Model W boson, $$W^\prime $$ masses below 5.1 TeV are excluded at the 95% confidence level.

Measurement of the isolated diphoton cross section in pp collisions at √s=7TeV with the ATLAS detector

Abstract: The ATLAS experiment has measured the production cross section of events with two isolated photons in the final state, in proton-proton collisions at √s=7 TeV. The full data set acquired in 2010 is used, corresponding to an integrated luminosity of 37 pb-1. The background, consisting of hadronic jets and isolated electrons, is estimated with fully data-driven techniques and subtracted. The differential cross sections, as functions of the di-photon mass (mγγ), total transverse momentum (pT,γγ), and azimuthal separation (Δϕγγ), are presented and compared to the predictions of next-to-leading-order QCD.

Measurements of the production cross section of a Z boson in association with jets in pp collisions at s√=13 TeV with the ATLAS detector

Abstract: Measurements of the production cross section of a Z boson in association with jets in proton-proton collisions at root s = 13TeV are presented, using data corresponding to an integrated luminosity

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.).