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 fragmentation fraction ratio fs/fd and its dependence on B meson kinematics

Abstract: The relative production rate of B0s and B 0 mesons is determined with the hadronic decays B0s→D−sπ+ and B 0 → D − K +. The measurement uses data corresponding to 1.0 fb−1 of pp collisions at a centre-of-mass energy of s√=7 TeV recorded in the forward region with the LHCb experiment. The ratio of production rates, f s /f d , is measured to be 0.238 ± 0.004 ± 0.015 ± 0.021, where the first uncertainty is statistical, the second systematic, and the third theoretical. This is combined with a previous LHCb measurement to obtain f s /f d = 0.256 ± 0.020. The dependence of f s /f d on the transverse momentum and pseudorapidity of the B meson is determined using the decays B0s→D−sπ+ and B 0 → D −π+. There is evidence for a decrease with increasing transverse momentum, whereas the ratio remains constant as a function of pseudorapidity. In addition, the ratio of branching fractions of the decays B 0 → D − K + and B 0 → D −π+ is measured to be 0.0822 ± 0.0011 (stat) ± 0.0025 (syst).

Search for heavy neutrinos and W bosons with right-handed couplings in proton-proton collisions at sqrt(s) = 8 TeV

Abstract: A search for heavy, right-handed neutrinos, $$\mathrm {N}_{\ell }$$ ( $$\ell = \mathrm {e}, \mu $$ ), and right-handed $$\mathrm {W}_{\mathrm {R}}$$ bosons, which arise in the left-right symmetric extensions of the standard model, has been performed by the CMS experiment. The search was based on a sample of two lepton plus two jet events collected in proton–proton collisions at a center-of-mass energy of 8 $$\,\text {TeV}$$ corresponding to an integrated luminosity of 19.7 $$\mathrm{fb}^{-1}$$ . For models with strict left-right symmetry, and assuming only one $$\mathrm {N}_{\ell }$$ flavor contributes significantly to the $$\mathrm {W}_{\mathrm {R}}$$ decay width, the region in the two-dimensional $$(M_{\mathrm {W}_{\mathrm {R}}}, M_{\mathrm {N}_{\ell }})$$ mass plane excluded at a 95 % confidence level extends to approximately $$M_{\mathrm {W}_{\mathrm {R}}} = 3.0\,\text {TeV} $$ and covers a large range of neutrino masses below the $$\mathrm {W}_{\mathrm {R}}$$ boson mass, depending on the value of $$M_{\mathrm {W}_{\mathrm {R}}}$$ . This search significantly extends the $$(M_{\mathrm {W}_{\mathrm {R}}}, M_{\mathrm {N}_{\ell }})$$ exclusion region beyond previous results.

Measurements of the nuclear modification factor for jets in Pb+Pb Collisions at √sNN = 2.76 TeV with the ATLAS detector

Abstract: Measurements of inclusive jet production are performed in pp and Pb+Pb collisions at √sNN = 2.76 TeV with the ATLAS detector at the LHC, corresponding to integrated luminosities of 4.0 pb−1 and 0.14 nb−1, respectively. The jets are identified with the anti-kt algorithm with R = 0.4, and the spectra are measured over the kinematic range of jet transverse momentum 32 < pT < 500 GeV, and absolute rapidity |y| < 2.1 and as a function of collision centrality. The nuclear modification factor, RAA, is evaluated and jets are found to be suppressed by approximately a factor of two in central collisions compared to pp collisions. The RAA shows a slight increase with pT and no significant variation with rapidity.

Inclusive search for a vector-like T quark with charge 2/3 in pp collisions at √s=8 TeV

Abstract: A search is performed for a massive new vector-like quark T, with charge (2/3), that is pair produced together with its antiparticle in proton–proton collisions. The data were collected by the CMS experiment at the Large Hadron Collider in 2012 at √s = 8 TeV and correspond to an integrated luminosity of 19.5 fb^(−1). The T quark is assumed to decay into three different final states, bW, tZ, and tH. The search is carried out using events with at least one isolated lepton. No deviations from standard model expectations are observed, and lower limits are set on the T quark mass at 95% confidence level. The lower limit lies between 687 and 782 GeV for all possible values of the branching fractions into the three different final states assuming strong production. These limits are the most stringent constraints to date on the existence of such a quark.

Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems

Abstract: This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.

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.).