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 squarks and gluinos in events with an isolated lepton, jets and missing transverse momentum at sqrt{s} = 13 TeV with the ATLAS detector

Abstract: The results of a search for squarks and gluinos in final states with an isolated electron or muon, multiple jets and large missing transverse momentum using proton-proton collision data at a center-of-mass energy of s=13 TeV are presented. The data set used was recorded during 2015 and 2016 by the ATLAS experiment at the Large Hadron Collider and corresponds to an integrated luminosity of 36.1 fb-1. No significant excess beyond the expected background is found. Exclusion limits at 95% confidence level are set in a number of supersymmetric scenarios, reaching masses up to 2.1 TeV for gluino pair production and up to 1.25 TeV for squark pair production.

First measurement of the quark-to-photon fragmentation function

Abstract: Earlier measurements at LEP of isolated hard photons in hadronic Z decays, attributed to radiation from primary quark pairs, have been extended in the ALEPH experiment to include hard photon productioninside hadron jets. Events are selected where all particles combine democratically to form hadron jets, one of which contains a photon with a fractional energyz≥0.7. After statistical subtraction of non-prompt photons, the quark-to-photon fragmentation function,D(z), is extracted directly from the measured 2-jet rate. By taking into account the perturbative contributions toD(z) obtained from anO(ααs) QCD calculation, the unknown non-perturbative component ofD(z) is then determined at highz. Provided due account is taken of hadronization effects nearz=1, a good description of the other event topologies is then found.

Search for new resonances decaying via WZ to leptons in proton-proton collisions at √s=8TeV

Abstract: A search is performed in proton-proton collisions at root s = 8 TeV for exotic particles decaying via WZ to fully leptonic final states with electrons, muons, and neutrinos. The data set corresponds to an integrated luminosity of 19.5 fb(-1). No significant excess is observed above the expected standard model background. Upper bounds at 95% confidence level are set on the production cross section of a W boson as predicted by an extended gauge model, and on the W'WZ coupling. The expected and observed mass limits for a W' boson, as predicted by this model, are 1.55 and 1.47 TeV, respectively. Stringent limits are also set in the context of low-scale technicolor models under a range of assumptions for the model parameters. (C) 2014 The Authors. Published by Elsevier B.V.

Search for supersymmetry in events with b-tagged jets and missing transverse momentum in pp collisions at √s=13 TeV with the ATLAS detector

Abstract: A search for the supersymmetric partners of the Standard Model bottom and top quarks is presented. The search uses 36.1 fb$^{−1}$ of pp collision data at $ \sqrt{s}=13 $ TeV collected by the ATLAS experiment at the Large Hadron Collider. Direct production of pairs of bottom and top squarks ( $ {\overline{b}}_1 $ and $ {\overline{t}}_1 $ ) is searched for in final states with b-tagged jets and missing transverse momentum. Distinctive selections are defined with either no charged leptons (electrons or muons) in the final state, or one charged lepton. The zero-lepton selection targets models in which the $ {\overline{b}}_1 $ is the lightest squark and decays via $ {\overline{b}}_1\to b{\overline{\chi}}_1^0 $ , where $ {\overline{\chi}}_1^0 $ is the lightest neutralino. The one-lepton final state targets models where bottom or top squarks are produced and can decay into multiple channels, $ {\overline{b}}_1\to b{\overline{\chi}}_1^0 $ and $ {\overline{b}}_1\to t{\overline{\chi}}_1^{\pm } $ , or $ {\overline{t}}_1\to t{\overline{\chi}}_1^0 $ and $ {\overline{t}}_1\to b{\overline{\chi}}_1^{\pm } $ , where $ {\overline{\chi}}_1^{\pm } $ is the lightest chargino and the mass difference $ {m}_{{\overline{\chi}}_1^{\pm }}-{m}_{{\overline{\chi}}_1^0} $ is set to 1 GeV. No excess above the expected Standard Model background is observed. Exclusion limits at 95% confidence level on the mass of third-generation squarks are derived in various supersymmetry-inspired simplified models.

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.).