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.

Search for squarks and gluinos with the ATLAS detector in final states with jets and missing transverse momentum using 4.7 fb − 1 of s = 7 TeV proton-proton collision data

Abstract: A search for squarks and gluinos in final states containing jets, missing transverse momentum and no high-p(T) electrons or muons is presented. The data represent the complete sample recorded in 2011 by the ATLAS experiment in 7 TeV proton-proton collisions at the Large Hadron Collider, with a total integrated luminosity of 4.7 fb(-1). No excess above the Standard Model background expectation is observed. Gluino masses below 860 GeV and squark masses below 1320 GeV are excluded at the 95% confidence level in simplified models containing only squarks of the first two generations, a gluino octet and a massless neutralino, for squark or gluino masses below 2 TeV, respectively. Squarks and gluinos with equal masses below 1410 GeV are excluded. In minimal supergravity/constrained minimal supersymmetric Standard Model models with tan beta = 10, A(0) = 0 and mu > 0, squarks and gluinos of equal mass are excluded for masses below 1360 GeV. Constraints are also placed on the parameter space of supersymmetric models with compressed spectra. These limits considerably extend the region of supersymmetric parameter space excluded by previous measurements with the ATLAS detector.

Searches for Higgs boson pair production in the hh →bbττ, γγWW∗, γγbb, bbbb channels with the ATLAS detector

Abstract: Searches for both resonant and nonresonant Higgs boson pair production are performed in the hh -> bb tau tau, gamma gamma WW* final states using 20.3 fb(-1) of pp collision data at a center-of-m ...

Search for the Standard Model Higgs boson produced in association with top quarks and decaying into bb¯ in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: A search for the Standard Model Higgs boson produced in association with a top-quark pair, , is presented. The analysis uses 20.3 fb(-1) of pp collision data at , collected with the ATLAS detector at the Large Hadron Collider during 2012. The search is designed for the decay mode and uses events containing one or two electrons or muons. In order to improve the sensitivity of the search, events are categorised according to their jet and b-tagged jet multiplicities. A neural network is used to discriminate between signal and background events, the latter being dominated by +jets production. In the single-lepton channel, variables calculated using a matrix element method are included as inputs to the neural network to improve discrimination of the irreducible background. No significant excess of events above the background expectation is found and an observed (expected) limit of 3.4 (2.2) times the Standard Model cross section is obtained at 95 % confidence level. The ratio of the measured signal cross section to the Standard Model expectation is found to be assuming a Higgs boson mass of 125 Gev.

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.).