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.

Measurements of Higgs boson properties in the diphoton decay channel with 36 fb−1 of pp collision data at s√=13 TeV with the ATLAS detector

Abstract: Properties of the Higgs boson are measured in the two-photon final state using 36.1 fb-1 of proton? proton collision data recorded at ffiffi √s = 13 TeV by the ATLAS experiment at the Large Hadron Collider. Cross-section measurements for the production of a Higgs boson through gluon-gluon fusion, vectorboson fusion, and in association with a vector boson or a top-quark pair are reported. The signal strength, defined as the ratio of the observed to the expected signal yield, is measured for each of these production processes as well as inclusively. The global signal strength measurement of 0.99 ± 0.14 improves on the precision of the ATLAS measurement at √s = 7 and 8 TeV by a factor of two. Measurements of gluon-gluon fusion and vector-boson fusion productions yield signal strengths compatible with the Standard Model prediction. Measurements of simplified template cross sections, designed to quantify the different Higgs boson production processes in specific regions of phase space, are reported. The cross section for the production of the Higgs boson decaying to two isolated photons in a fiducial region closely matching the experimental selection of the photons is measured to be 55 ± 10 fb, which is in good agreement with the Standard Model prediction of 64 ± 2 fb. Furthermore, cross sections in fiducial regions enriched in Higgs boson production in vector-boson fusion or in association with large missing transverse momentum, leptons or top-quark pairs are reported. Differential and double-differential measurements are performed for several variables related to the diphoton kinematics as well as the kinematics and multiplicity of the jets produced in association with a Higgs boson. These differential cross sections are sensitive to higher order QCD corrections and properties of the Higgs boson, such as its spin and CP quantum numbers. No significant deviations from a wide array of Standard Model predictions are observed. Finally, the strength and tensor structure of the Higgs boson interactions are investigated using an effective Lagrangian, which introduces additional CP-even and CP-odd interactions. No significant new physics contributions are observed.

Measurement of the inclusive W± and Z/γ∗ cross sections in the e and μ decay channels in pp collisions at √s=7TeV with the ATLAS detector

Abstract: The production cross sections of the inclusive Drell-Yan processes W-+/- -> l nu and Z/gamma* -> ll (l = e, mu) are measured in proton-proton collisions at root s = 7 TeV with the ATLAS detector. The cross sections are reported integrated over a fiducial kinematic range, extrapolated to the full range, and also evaluated differentially as a function of the W decay lepton pseudorapidity and the Z boson rapidity, respectively. Based on an integrated luminosity of about 35 pb(-1) collected in 2010, the precision of these measurements reaches a few percent. The integrated and the differential W-+/- and Z/gamma* cross sections in the e and mu channels are combined, and compared with perturbative QCD calculations, based on a number of different parton distribution sets available at next-to-next-to-leading order.

Search for direct third-generation squark pair production in final states with missing transverse momentum and two b-jets in √s = 8 TeV pp collisions with the ATLAS detector

Abstract: The results of a search for pair production of supersymmetric partners of the Standard Model third-generation quarks are reported. This search uses 20.1 fb-1 of pp collisions at sqrt{s}=8 TeV collected by the ATLAS experiment at the Large Hadron Collider. The lightest bottom and top squarks (b1 and t1 respectively) are searched for in a final state with large missing transverse momentum and two jets identified as originating from b-quarks. No excess of events above the expected level of Standard Model background is found. The results are used to set upper limits on the visible cross section for processes beyond the Standard Model. Exclusion limits at the 95% confidence level on the masses of the third-generation squarks are derived in phenomenological supersymmetric R-parity-conserving models in which either the bottom or the top squark is the lightest squark. The b1 is assumed to decay via b1->b chi0 and the t via t1->b chipm, with undetectable products of the subsequent decay of the chipm due to the small mass splitting between the chipm and the chi0.

Search for new phenomena in dijet events using 37 fb-1 of pp collision data collected at √s=13 TeV with the ATLAS detector

Abstract: Dijet events are studied in the proton-proton collision dataset recorded at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider in 2015 and 2016, corresponding to integrated lumino ...

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.).