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 diffractive dissociation cross sections in pp collisions at s =7TeV

Abstract: Measurements of diffractive dissociation cross sections in pp collisions at sqrt(s) = 7 TeV are presented in kinematic regions defined by the masses M[X] and M[Y] of the two final-state hadronic systems separated by the largest rapidity gap in the event. Differential cross sections are measured as a function of xi[X]= M[X]^2/s in the region -5.5 3, log[10]M[X] > 1.1, and log[10]M[Y] > 1.1, a region dominated by DD. The cross sections integrated over these regions are found to be, respectively, 2.99 +/- 0.02 (stat) +0.32 -0.29 (syst) mb, 1.18 +/- 0.02 (stat) +/- 0.13 (syst) mb, and 0.58 +/- 0.01 (stat) +0.13 -0.11 (syst) mb, and are used to extract total SD and DD cross sections. In addition, the inclusive differential cross section, d sigma /d Delta eta[F], for events with a pseudorapidity gap adjacent to the edge of the detector, is measured over Delta eta[F] = 8.4 units of pseudorapidity. The results are compared to those of other experiments and to theoretical predictions, and found compatible with slowly-rising diffractive cross sections as a function of center-of-mass energy.

Evidence of a $J/\psi \varLambda$ structure and observation of excited $\varXi^-$ states in the $\varXi_b^-\to J/\psi \varLambda K^-$ decay

Abstract: First evidence of a structure in the $J/\psi \varLambda$ invariant mass distribution is obtained from an amplitude analysis of $\varXi_b^- \to J/\psi \varLambda K^-$ decays. The observed structure is consistent with being due to a charmonium pentaquark with strangeness. Its mass and width are determined to be $4458.8\pm2.9\,^{+4.7}_{-1.1}$MeV and $17.3\pm6.5\,^{+8.0}_{-5.7}$MeV, where the quoted uncertainties are statistical and systematic, respectively. The structure is also consistent with being due to two resonances. In addition, the narrow excited $\varXi^-$ states, $\varXi(1690)^-$ and $\varXi(1820)^-$, are seen for the first time in a $\varXi_b^-$ decay, and their masses and widths are measured with improved precision. The analysis is performed using $pp$ collision data corresponding to a total integrated luminosity of 9fb$^{-1}$, collected with the LHCb experiment at centre-of-mass energies of $7$, $8$ and $13$TeV.

ALMA Observations of Warm Dense Gas in NGC 1614 — Breaking of Star Formation Law in the Central kpc

Abstract: We present ALMA Cycle-0 observations of the CO (6-5) line emission and of the 435 μm dust continuum emission in the central kpc of NGC 1614, a local luminous infrared galaxy (LIRG) at a distance of 67.8 Mpc (1" = 329 pc). The CO emission is well resolved by the ALMA beam (0."26×0."20) into a circum-nuclear ring, with an integrated flux of f_(CO (6-5)) = 898 (±153) Jy km s^(-1), which is 63(±12)% of the total CO (6-5) flux measured by Herschel. The molecular ring, located between 100 pc < r < 350 pc from the nucleus, looks clumpy and includes seven unresolved (or marginally resolved) knots with median velocity dispersion of ∼ 40 km s^(-1). These knots are associated with strong star formation regions with Σ_(SFR) ∼ 100 M_⊙ yr^(-1) kpc^(-2) and Σ_(Gas) ∼ 10^4 M_⊙ pc^(-2). The non-detections of the nucleus in both the CO (6-5) line emission and the 435 μm continuum rule out, with relatively high confidence, a Compton-thick AGN in NGC 1614. Comparisons with radio continuum emission show a strong deviation from an expected local correlation between Σ_(Gas) and Σ_(SFR), indicating a breakdown of the Kennicutt-Schmidt law on the linear scale of ∼100 pc.

Search for stopped long-lived particles produced in pp collisions at √s = 7 TeV

Abstract: A search has been performed for long-lived particles that have stopped in the CMS detector, during 7 TeV proton-proton operations of the CERN LHC. The existence of such particles could be inferred from observation of their decays when there were no proton-proton collisions in the CMS detector, namely during gaps between LHC beam crossings. Using a data set in which CMS recorded an integrated luminosity of 4.0 inverse femtobarns, and a search interval corresponding to 246 hours of trigger live time, 12 events are observed, with a mean background prediction of 8.6+/-2.4 events. Limits are presented at 95% confidence level on long-lived gluino and stop production, over 13 orders of magnitude of particle lifetime. Assuming the "cloud model" of R-hadron interactions, a gluino with mass below 640 GeV and a stop with mass below 340 GeV are excluded, for lifetimes between 10 microseconds and 1000 seconds.

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.).