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 pair production of a heavy up-type quark decaying to a W boson and a b quark in the lepton+jets channel with the ATLAS detector

Abstract: A search is presented for production of a heavy up-type quark (t') together with its antiparticle, assuming subsequent decay to a W boson and a b quark, t'(t) over bar' -> W(+)bW(-)(b) over bar. The search is based on 1.04 fb(-1) of proton-proton collisions at root s = 7 TeV collected by the ATLAS detector at the CERN Large Hadron Collider. Data are analyzed in the lepton + jets final state, characterized by a high transverse momentum isolated electron or muon, high missing transverse momentum, and at least three jets. No significant excess of events above the background expectation is observed. A 95% C.L. lower limit of 404 GeV is set for the mass of the t' quark.

Search for single production of vector-like quarks decaying into Wb in pp collisions at √s = 13 TeV with the ATLAS detector

Abstract: A search for singly produced vector-like quarks Q, where Q can be either a T quark with charge +2/3 or a Y quark with charge −4/3, is performed in proton–proton collision data at a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of 36.1 fb−1, recorded with the ATLAS detector at the LHC in 2015 and 2016. The analysis targets Q → Wb decays where the W boson decays leptonically. No significant deviation from the expected Standard Model background is observed. Upper limits are set on the QWb coupling strength and the mixing between the Standard Model sector and a singlet T quark or a Y quark from a (B, Y) doublet or a (T, B, Y) triplet, taking into account the interference effects with the Standard Model background. The upper limits set on the mixing angle are as small as |sin θL| = 0.18 for a singlet T quark of mass 800 GeV, |sin θR| = 0.17 for a Y quark of mass 800 GeV in a (B, Y) doublet model and |sin θL| = 0.16 for a Y quark of mass 800 GeV in a (T, B, Y) triplet model. Within a (B, Y) doublet model, the limits set on the mixing parameter |sin θR| are comparable with the exclusion limits from electroweak precision observables in the mass range between about 900 GeV and 1250 GeV.

Measurement of the top pair production cross-section in 8 TeV proton-proton collisions using kinematic information in the lepton+jets final state with ATLAS

Abstract: A measurement is presented of the (tt) over bar inclusive production cross section in pp collisions at a center-ofmass energy of pffisffiffi root s = 8 TeV using data collected by the ATLAS detector at the CERN Large Hadron Collider. The measurement was performed in the lepton + jets final state using a data set corresponding to an integrated luminosity of 20.3 fb(-1). The cross section was obtained using a likelihood discriminant fit and b-jet identification was used to improve the signal-to-background ratio. The inclusive (tt) over bar production cross section was measured to be 260 +/- 1(stat)(-23)(+22)(stat) +/- 8(lumi) +/- 4(beam) pb assuming a top-quark mass of 172.5 GeV, in good agreement with the theoretical prediction of 253(-15)(+13) pb. The (tt) over bar -> (e, mu) + jets production cross section in the fiducial region determined by the detector acceptance is also reported.

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.).