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.

Searches for supersymmetry using the M-T2 variable in hadronic events produced in pp collisions at 8 TeV

Abstract: Searches for supersymmetry (SUSY) are performed using a sample of hadronic events produced in 8 TeV pp collisions at the CERN LHC. The searches are based on the MT2 variable, which is a measure of the transverse momentum imbalance in an event. The data were collected with the CMS detector and correspond to an integrated luminosity of 19.5 inverse femtobarns. Two related searches are performed. The first is an inclusive search based on signal regions defined by the value of the MT2 variable, the hadronic energy in the event, the jet multiplicity, and the number of jets identified as originating from bottom quarks. The second is a search for a mass peak corresponding to a Higgs boson decaying to a bottom quark-antiquark pair, where the Higgs boson is produced as a decay product of a SUSY particle. For both searches, the principal backgrounds are evaluated with data control samples. No significant excess over the expected number of background events is observed, and exclusion limits on various SUSY models are derived.

Search for the standard model Higgs boson produced in association with top quarks and decaying into a bb¯ pair in pp collisions at √s=13 TeV with the ATLAS detector

Abstract: A search for the standard model Higgs boson produced in association with a top-quark pair, t(t)overbarH, is presented. The analysis uses 36.1 fb(-1) of pp collision data at root s = 13 TeV collecte ...

Search for Higgs boson pair production in the [Formula: see text] final state from pp collisions at [Formula: see text] TeVwith the ATLAS detector.

Abstract: A search for Higgs boson pair production [Formula: see text] is performed with 19.5 fb[Formula: see text] of proton-proton collision data at [Formula: see text] TeV, which were recorded by the ATLAS detector at the Large Hadron Collider in 2012. The decay products of each Higgs boson are reconstructed as a high-momentum [Formula: see text] system with either a pair of small-radius jets or a single large-radius jet, the latter exploiting jet substructure techniques and associated b-tagged track-jets. No evidence for resonant or non-resonant Higgs boson pair production is observed. The data are interpreted in the context of the Randall-Sundrum model with a warped extra dimension as well as the two-Higgs-doublet model. An upper limit on the cross-section for [Formula: see text] of 3.2 (2.3) fb is set for a Kaluza-Klein graviton [Formula: see text] mass of 1.0 (1.5) TeV, at the 95 % confidence level. The search for non-resonant Standard Model hh production sets an observed 95 % confidence level upper limit on the production cross-section [Formula: see text] of 202 fb, compared to a Standard Model prediction of [Formula: see text] fb.

Measurement of the Υ(1S), Υ(2S), and Υ(3S) Polarizations in pp Collisions at s√=7 TeV

Abstract: The polarizations of the Υ(1S), Υ(2S), and Υ(3S) mesons are measured in proton-proton collisions at √s=7 TeV, using a data sample of Υ(nS)→μ^+μ^- decays collected by the CMS experiment, corresponding to an integrated luminosity of 4.9 fb^(-1). The dimuon decay angular distributions are analyzed in three different polarization frames. The polarization parameters λ_ϑ, λ_φ, and λ_(ϑφ), as well as the frame-invariant quantity λ˜, are presented as a function of the Υ(nS) transverse momentum between 10 and 50 GeV, in the rapidity ranges |y|<0.6 and 0.6<|y|<1.2. No evidence of large transverse or longitudinal polarizations is seen in the explored kinematic region.

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.).