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.

Event activity dependence of Υ(nS) production in √sNN = 5.02 TeV pPb and √s = 2.76 TeV pp collisions

Abstract: The production of Y(1S), Y(2S), and Y(3S) is investigated in pPb and pp collisions at centre-of-mass energies per nucleon pair of 5.02 TeV and 2.76 TeV, respectively. The datasets correspond to integrated luminosities of about 31 inverse nanobarns (pPb) and 5.4 inverse picobarns (pp), collected in 2013 by the CMS experiment at the LHC. Upsilons that decay into muons are reconstructed within the rapidity interval abs(y[CM]) , are found to rise with both measures of the event activity in pp and pPb. In both collision systems, the ratios of the excited to the ground state cross sections, Y(nS)/Y(1S), are found to decrease with the charged-particle multiplicity, while as a function of the transverse energy the variation is less pronounced. The event activity integrated double ratios, [Y(nS)/Y(1S)][pPb] / [Y(nS)/Y(1S)][pp], are also measured and found to be 0.83 +/- 0.05 (stat.) +/- 0.05 (syst.) and 0.71 +/- 0.08 (stat.) +/- 0.09 (syst.) for Y(2S) and Y(3S), respectively.

Cross-section measurements of the Higgs boson decaying into a pair of τ-leptons in proton-proton collisions at √s=13 TeV with the ATLAS detector

Abstract: A measurement of production cross sections of the Higgs boson in proton-proton collisions is presented in the H→ττ decay channel. The analysis is performed using 36.1 fb-1 of data recorded by the ATLAS experiment at the Large Hadron Collider at a center-of-mass energy of s=13 TeV. All combinations of leptonic (τ→vv with =e,μ) and hadronic (τ→hadrons v) τ decays are considered. The H→ττ signal over the expected background from other Standard Model processes is established with an observed (expected) significance of 4.4 (4.1) standard deviations. Combined with results obtained using data taken at 7 and 8 TeV center-of-mass energies, the observed (expected) significance amounts to 6.4 (5.4) standard deviations and constitutes an observation of H→ττ decays. Using the data taken at s=13 TeV, the total cross section in the H→ττ decay channel is measured to be 3.77-0.59+0.60 (stat) -0.74+0.87 (syst) pb, for a Higgs boson of mass 125 GeV assuming the relative contributions of its production modes as predicted by the Standard Model. Total cross sections in the H→ττ decay channel are determined separately for vector-boson-fusion production and gluon-gluon-fusion production to be σH→ττVBF=0.28±0.09 (stat) -0.09+0.11 (syst) pb and σH→ττggF=3.1±1.0 (stat) -1.3+1.6 (syst) pb, respectively. Similarly, results of a fit are reported in the framework of simplified template cross sections. All measurements are in agreement with Standard Model expectations.

Search for heavy ZZ resonances in the l(+) l(-) l(+) l(-) and l(+) l(-) nu(nu)over-bar final states using proton-proton collisions at root s=13 TeV with the ATLAS detector

Abstract: A search for heavy resonances decaying into a pair of $$Z$$ bosons leading to $$\ell ^+\ell ^-\ell ^+\ell ^-$$ and $$\ell ^+\ell ^- u \bar{ u }$$ final states, where $$\ell $$ stands for either an electron or a muon, is presented. The search uses proton–proton collision data at a centre-of-mass energy of 13 $$\text {TeV}$$ corresponding to an integrated luminosity of 36.1 $$\hbox {fb}^{-1}$$ collected with the ATLAS detector during 2015 and 2016 at the Large Hadron Collider. Different mass ranges for the hypothetical resonances are considered, depending on the final state and model. The different ranges span between 200 and 2000 $$\text {GeV}$$ . The results are interpreted as upper limits on the production cross section of a spin-0 or spin-2 resonance. The upper limits for the spin-0 resonance are translated to exclusion contours in the context of Type-I and Type-II two-Higgs-doublet models, while those for the spin-2 resonance are used to constrain the Randall–Sundrum model with an extra dimension giving rise to spin-2 graviton excitations.

Precision Higgs Physics at CEPC

Abstract: The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS and CMS Collaborations marked the beginning of a new era in high energy physics. The Higgs boson will be the subject of extensive studies of the ongoing LHC program. At the same time, lepton collider based Higgs factories have been proposed as a possible next step beyond the LHC, with its main goal to precisely measure the properties of the Higgs boson and probe potential new physics associated with the Higgs boson. The Circular Electron Positron Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an $e^+e^-$ circular collider proposed by and to be hosted in China. Located in a tunnel of approximately 100~km in circumference, it will operate at a center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we present the first estimates on the precision of the Higgs boson property measurements achievable at the CEPC and discuss implications of these measurements.

Search for a supersymmetric partner to the top quark in final states with jets and missing transverse momentum at √s=7 TeV with the ATLAS Detector

Abstract: A search for direct pair production of supersymmetric top squarks ((t) over tilde (1)) is presented, assuming the (t) over tilde (1) decays into a top quark and the lightest supersymmetric particle, (chi) over tilde (0)(1), and that both top quarks decay to purely hadronic final states. A total of 16 (4) events are observed compared to a predicted standard model background of 13.5(-3.6)(+3.7) (4.4(-1.3)(+1.7)) events in two signal regions based on integral Ldt = 4.7 fb(-1) of pp collision data taken at root s = 7 TeV with the ATLAS detector at the LHC. An exclusion region in the (t) over tilde (1) versus (chi) over tilde (0)(1) mass plane is evaluated: 370 1) 10) similar to 0 GeV while m((t) over tilde1) = 445 GeV is excluded for m((chi) over tilde 10) <= 50 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.).