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.

Observation of a New Ξ b Baryon

Abstract: The first observation of a new b baryon via its strong decay into Xi(b)^- pi^+ (plus charge conjugates) is reported. The measurement uses a data sample of pp collisions at sqrt(s) = 7 TeV collected by the CMS experiment at the LHC, corresponding to an integrated luminosity of 5.3 inverse femtobarns. The known Xi(b)^- baryon is reconstructed via the decay chain Xi(b)^- to J/psi Xi^- to mu^+ mu^- Lambda^0 pi^-, with Lambda^0 to p pi^-. A peak is observed in the distribution of the difference between the mass of the Xi(b)^- pi^+ system and the sum of the masses of the Xi(b)^- and pi^+, with a significance exceeding five standard deviations. The mass difference of the peak is 14.84 +/- 0.74 (stat.) +/- 0.28 (syst.) MeV. The new state most likely corresponds to the J^P=3/2^+ companion of the Xi(b).

Further search for supersymmetry at √s=7TeV in final states with jets, missing transverse momentum, and isolated leptons with the ATLAS detector

Abstract: This work presents a new inclusive search for supersymmetry (SUSY) by the ATLAS experiment at the LHC in proton-proton collisions at a center-of-mass energy root s = 7 TeV in final states with jets, missing transverse momentum and one or more isolated electrons and/or muons. The search is based on data from the full 2011 data-taking period, corresponding to an integrated luminosity of 4.7 fb(-1). Single-lepton and multilepton channels are treated together in one analysis. An increase in sensitivity is obtained by simultaneously fitting the number of events in statistically independent signal regions, and the shapes of distributions within those regions. A dedicated signal region is introduced to be sensitive to decay cascades of SUSY particles with small mass differences ("compressed SUSY"). Background uncertainties are constrained by fitting to the jet-multiplicity distribution in background control regions. Observations are consistent with Standard Model expectations, and limits are set or extended on a number of SUSY models.

Search for pair produced fourth-generation up-type quarks in pp collisions at √s=7 TeV with a lepton in the final state

Abstract: The results of a search for the pair production of a fourth-generation up-type quark (t′t′) in proton–proton collisions at View the MathML sources=7 TeV are presented, using data corresponding to an integrated luminosity of about 5.0 fb−1 collected by the Compact Muon Solenoid experiment at the LHC. The t′t′ quark is assumed to decay exclusively to a W boson and a b quark. Events with a single isolated electron or muon, missing transverse momentum, and at least four hadronic jets, of which at least one must be identified as a b jet, are selected. No significant excess of events over standard model expectations is observed. Upper limits for the View the MathML sourcet′t¯′ production cross section at 95% confidence level are set as a function of t′t′ mass, and t′t′-quark production for masses below 570 GeV is excluded. The search is equally sensitive to nonchiral heavy quarks decaying to Wb. In this case, the results can be interpreted as upper limits on the production cross section times the branching fraction to Wb.

Nuclear effects on the transverse momentum spectra of charged particles in pPb collisions at √sNN = 5.02 TeV

Abstract: Transverse momentum spectra of charged particles are measured by the CMS experiment at the CERN LHC in pPb collisions at sqrt(s[NN]) = 5.02 TeV, in the range 0.4 20 GeV compared to expectations from the pp reference. The enhancement is larger than predicted by perturbative quantum chromodynamics calculations that include antishadowing modifications of nPDFs.

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.).