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 large missing transverse momentum in association with one top-quark in proton-proton collisions at √s = 13 TeV with the ATLAS detector

Abstract: This paper describes a search for events with one top-quark and large missing transverse momentum in the final state. Data collected during 2015 and 2016 by the ATLAS experiment from 13 TeV proton–proton collisions at the LHC corresponding to an integrated luminosity of 36.1 fb−1 are used. Two channels are considered, depending on the leptonic or the hadronic decays of the W boson from the top quark. The obtained results are interpreted in the context of simplified models for dark-matter production and for the single production of a vector-like T quark. In the absence of significant deviations from the Standard Model background expectation, 95% confidence-level upper limits on the corresponding production cross-sections are obtained and these limits are translated into constraints on the parameter space of the models considered.

Measurement of the production cross-section of ψ(2S) →J/ψ (→ μ + μ¯)π + π¯ in pp collisions at √s = 7 TeV at ATLAS

Abstract: The prompt and non-prompt production cross-sections for psi(2S) mesons are measured using 2.1 fb(-1) of pp collision data at a centre-of-mass energy of 7TeV recorded by the ATLAS experiment at the LHC. The measurement exploits the psi(2S) --> J/psi(--> mu(+)mu(-)) pi(+)pi(-) decay mode, and probes psi(2S) mesons with transverse momenta in the range 10 <= p(T) < 100 GeV and rapidity |y| < 2.0. The results are compared to other measurements of psi(2S) production at the LHC and to various theoretical models for prompt and non-prompt quarkonium production.

Time-dependent angular analysis of the decay B-s(0) -> J/psi phi and extraction of Delta Gamma(s) and the CP-violating weak phase phi(s) by ATLAS

Abstract: A measurement of B-s(0) -> J/psi phi decay parameters, including the CP-violating weak phase phi(s) and the decay width difference Delta Gamma(s) is reported, using 4.9 fb(-1) of integrated luminosity collected in 2011 by the ATLAS detector from LHC pp collisions at a centre-of-mass energy root s = 7 TeV. The mean decay width Gamma(s) and the transversity amplitudes vertical bar A(0)(0)vertical bar(2) and vertical bar A(parallel to)(0)vertical bar(2) are also measured. The values reported for these parameters are: phi(s) = 0.22 +/- 0.41 (stat.) +/- 0.10 (syst.) rad Delta Gamma(s) = 0.053 +/- 0.021 (stat.) +/- 0.010 (syst.) ps(-1) Gamma(s) = 0.677 +/- 0.007 (stat.) +/- 0.004 (syst.) ps(-1) vertical bar A(0)(0)vertical bar(2) = 0.528 +/- 0.006 (stat.) +/- 0.009 (syst.) vertical bar A(parallel to)(0)vertical bar(2) = 0.220 +/- 0.008 (stat.) +/- 0.007 (syst.) where the values quoted for phi(s) and Delta Gamma(s) correspond to the solution compatible with the external measurements to which the strong phase delta(perpendicular to) is constrained and where is Delta Gamma(s) constrained to be positive. The fraction of S-wave KK or f(0) contamination through the decays B-s(0) -> J/psi K+K- (f(0)) is measured as well and is found to be consistent with zero. Results for phi(s) and Delta Gamma(s) are also presented as 68%, 90% and 95% likelihood contours, which show agreement with Standard Model expectations.

Measurement of the shape of the Lambda(0)(b) ->+ Lambda(+)(c) mu(-)(nu)over-bar mu differential decay rate

Abstract: A measurement of the shape of the differential decay rate and the associated Isgur-Wise function for the decay Λ0 b → Λ+c μ- νμ is reported, using data corresponding to 3 fb-1collected with the LHCb detector in proton-proton collisions. The Λ+c μ- νμ anything final states are reconstructed through the detection of a muon and a Λ+c baryon decaying into pK-π+, and the decays Λ0 b → Λ+c π+π-μ- νμ are used to determine contributions from Λ0 b → Λ0c μ- νμ decays. The measured dependence of the differential decay rate upon the squared four-momentum transfer between the heavy baryons, q2, is compared with expectations from heavy-quark effective theory and from unquenched lattice QCD predictions.

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.).