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 top quark mass in the tt → lepton+jets and tt → dilepton channels using √s = 7 TeV ATLAS data

Abstract: The top quark mass was measured in the channels t (t) over bar -> lepton+jets and t (t) over bar -> dilepton (lepton = e, mu) based on ATLAS data recorded in 2011. The data were taken at the LHC with a proton-proton centre-of-mass energy of root s = 7 TeV and correspond to an integrated luminosity of 4.6 fb(-1). The t (t) over bar -> lepton+jets analysis uses a three-dimensional template technique which determines the top quark mass together with a global jet energy scale factor (JSF), and a relative b-to-light-jet energy scale factor (bJSF), where the terms b-jets and light-jets refer to jets originating from b-quarks and u,d,c, s-quarks or gluons, respectively. The analysis of the t (t) over bar -> dilepton channel exploits a one-dimensional template method using the m(lb) observable, defined as the average invariant mass of the two lepton+b-jet pairs in each event. The top quark mass is measured to be 172.33 +/- 0.75(stat + JSF + bJSF) +/- 1.02(syst) GeV, and 173.79 +/- 0.54(stat) +/- 1.30(syst) GeV in the t (t) over bar -> lepton+jets and t (t) over bar -> dilepton channels, respectively. The combination of the two results yields m(top) = 172.99 +/- 0.48(stat) +/- 0.78(syst) GeV, with a total uncertainty of 0.91 GeV.

CP Properties of Higgs Boson Interactions with Top Quarks in the tt[over ¯]H and tH Processes Using H→γγ with the ATLAS Detector.

Abstract: A study of the charge conjugation and parity (CP) properties of the interaction between the Higgs boson and top quarks is presented. Higgs bosons are identified via the diphoton decay channel (H→γγ), and their production in association with a top quark pair (tt[over ¯]H) or single top quark (tH) is studied. The analysis uses 139 fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of sqrt[s]=13 TeV with the ATLAS detector at the Large Hadron Collider. Assuming a CP-even coupling, the tt[over ¯]H process is observed with a significance of 5.2 standard deviations. The measured cross section times H→γγ branching ratio is 1.64_{-0.36}^{+0.38}(stat)_{-0.14}^{+0.17}(sys) fb, and the measured rate for tt[over ¯]H is 1.43_{-0.31}^{+0.33}(stat)_{-0.15}^{+0.21}(sys) times the Standard Model expectation. The tH production process is not observed and an upper limit on its rate of 12 times the Standard Model expectation is set. A CP-mixing angle greater (less) than 43 (-43)° is excluded 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.).