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 distributions of event-by-event flow harmonics in lead-lead collisions at √SNN = 2.76 TeV with the ATLAS detector at the LHC

Abstract: The distributions of event-by-event harmonic flow coefficients v (n) for n = 2- 4 are measured in = 2.76 TeV Pb + Pb collisions using the ATLAS detector at the LHC. The measurements are performed u ...

Centrality dependence of dihadron correlations and azimuthal anisotropy harmonics in PbPb collisions at √SNN = 2.76 Tev

Abstract: Measurements from the CMS experiment at the LHC of dihadron correlations for charged particles produced in PbPb collisions at a nucleon-nucleon centre-of-mass energy of 2.76 TeV are presented. The results are reported as a function of the particle transverse momenta (pt) and collision centrality over a broad range in relative pseudorapidity [Delta(eta)] and the full range of relative azimuthal angle [Delta(phi)]. The observed two-dimensional correlation structure in Delta(eta) and Delta(phi) is characterised by a narrow peak at Delta(eta), Delta(phi) approximately (0, 0) from jet-like correlations and a long-range structure that persists up to at least |Delta(eta)| = 4. An enhancement of the magnitude of the short-range jet peak is observed with increasing centrality, especially for particles of pt around 1-2 GeV/c. The long-range azimuthal dihadron correlations are extensively studied using a Fourier decomposition analysis. The extracted Fourier coefficients are found to factorise into a product of single-particle azimuthal anisotropies up to pt approximately 3-3.5 GeV/c for at least one particle from each pair, except for the second-order harmonics in the most central PbPb events. Various orders of the single-particle azimuthal anisotropy harmonics are extracted for associated particle pt of 1-3 GeV/c, as a function of the trigger particle pt up to 20 GeV/c and over the full centrality range.

Charged particle multiplicities in pp interactions at sqrt(s) = 0.9, 2.36, and 7 TeV

Abstract: Measurements of primary charged hadron multiplicity distributions are presented for non-single-diffractive events in proton-proton collisions at centre-of-mass energies of sqrt(s) = 0.9, 2.36, and 7 TeV, in five pseudorapidity ranges from |eta|<0.5 to |eta|<2.4. The data were collected with the minimum-bias trigger of the CMS experiment during the LHC commissioning runs in 2009 and the 7 TeV run in 2010. The multiplicity distribution at sqrt(s) = 0.9 TeV is in agreement with previous measurements. At higher energies the increase of the mean multiplicity with sqrt(s) is underestimated by most event generators. The average transverse momentum as a function of the multiplicity is also presented. The measurement of higher-order moments of the multiplicity distribution confirms the violation of Koba-Nielsen-Olesen scaling that has been observed at lower energies.

Study of the rare decays of B s 0 and B 0 mesons into muon pairs using data collected during 2015 and 2016 with the ATLAS detector

Abstract: A study of the decays B0 s ! + and B0 ! + has been performed using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016. Since the detector resolut ...

Missing transverse energy performance of the CMS detector

Abstract: During 2010 the LHC delivered pp collisions with a centre-of-mass energy of 7 TeV. In this paper, the results of comprehensive studies of missing transverse energy as measured by the CMS detector are presented. The results cover the measurements of the scale and resolution for missing transverse energy, and the effects of multiple pp interactions within the same bunch crossings on the scale and resolution. Anomalous measurements of missing transverse energy are studied, and algorithms for their identification are described. The performances of several reconstruction algorithms for calculating missing transverse energy are compared. An algorithm, called missing-transverse-energy significance, which estimates the compatibility of the reconstructed missing transverse energy with zero, is described, and its performance is demonstrated.

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.).