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 narrow resonances and quantum black holes in inclusive and b-tagged dijet mass spectra from pp collisions at sqrt(s)=7 TeV

Abstract: A search for narrow resonances and quantum black holes is performed in inclusive and b-tagged dijet mass spectra measured with the CMS detector at the LHC. The data set corresponds to 5 inverse femtobarns of integrated luminosity collected in pp collisions at sqrt(s) = 7 TeV. No narrow resonances or quantum black holes are observed. Model-independent upper limits at the 95% confidence level are obtained on the product of the cross section, branching fraction into dijets, and acceptance for three scenarios: decay into quark-quark, quark-gluon, and gluon-gluon pairs. Specific lower limits are set on the mass of string resonances (4.31 TeV), excited quarks (3.32 TeV), axi-gluons and colorons (3.36 TeV), scalar color-octet resonances (2.07 TeV), E(6) diquarks (3.75 TeV), and on the masses of W' (1.92 TeV) and Z' (1.47 TeV) bosons. The limits on the minimum mass of quantum black holes range from 4 to 5.3 TeV. In addition, b-quark tagging is applied to the two leading jets and upper limits are set on the production of narrow dijet resonances in a model-independent fashion as a function of the branching fraction to b-jet pairs.

Search for microscopic black holes in pp collisions at $s$ = 8 TeV

Abstract: A search for microscopic black holes in pp collisions at a center-of-mass energy of 7 TeV is presented. The data sample corresponds to an integrated luminosity of 4.7 inverse femtobarns recorded by the CMS experiment at the LHC in 2011. Events with large total transverse energy have been analyzed for the presence of multiple energetic jets, leptons, and photons, which are typical signals of evaporating semiclassical and quantum black holes, and string balls. Agreement with the expected standard model backgrounds, which are dominated by QCD multijet production, has been observed for various combined multiplicities of jets and other reconstructed objects in the final state. Model-independent limits are set on new physics processes producing high-multiplicity, energetic final states. In addition, new model-specific indicative limits are set excluding semiclassical and quantum black holes with masses below 3.8 to 5.3 TeV and string balls with masses below 4.6 to 4.8 TeV. The analysis has a substantially increased sensitivity compared to previous searches.

Study of jet shapes in inclusive jet production in pp collisions at root s=7 TeV using the ATLAS detector

Abstract: Jet shapes have been measured in inclusive jet production in proton-proton collisions at root s = 7 TeV using 3 pb(-1) of data recorded by the ATLAS experiment at the LHC. Jets are reconstructed using the anti-k(t) algorithm with transverse momentum 30 GeV < p(T) < 600 GeV and rapidity in the region vertical bar y vertical bar < 2.8. The data are corrected for detector effects and compared to several leading-order QCD matrix elements plus parton shower Monte Carlo predictions, including different sets of parameters tuned to model fragmentation processes and underlying event contributions in the final state. The measured jets become narrower with increasing jet transverse momentum and the jet shapes present a moderate jet rapidity dependence. Within QCD, the data test a variety of perturbative and nonperturbative effects. In particular, the data show sensitivity to the details of the parton shower, fragmentation, and underlying event models in the Monte Carlo generators. For an appropriate choice of the parameters used in these models, the data are well described.

Search for Signatures of Extra Dimensions in the Diphoton Mass Spectrum at the Large Hadron Collider

Abstract: A search for signatures of extra spatial dimensions in the diphoton invariant-mass spectrum has been performed with the CMS detector at the LHC. No excess of events above the standard model expectation is observed using a data sample collected in proton-proton collisions at √s=7 TeV corresponding to an integrated luminosity of 2.2 fb(-1). In the context of the large-extra-dimensions model, lower limits are set on the effective Planck scale in the range of 2.3-3.8 TeV at the 95% confidence level. These limits are the most restrictive bounds on virtual-graviton exchange to date. The most restrictive lower limits to date are also set on the mass of the first graviton excitation in the Randall-Sundrum model in the range of 0.86-1.84 TeV, for values of the associated coupling parameter between 0.01 and 0.10.

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.).