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.

CO, H 2 O, H 2 O + line and dust emission in a z = 3.63 strongly lensed starburst merger at sub-kiloparsec scales

Abstract: Using the Atacama Large Millimeter/submillimeter Array (ALMA), we report high angular-resolution observations of the redshift z = 3.63 galaxy H-ATLAS J083051.0+013224 (G09v1.97), one of the most luminous strongly lensed galaxies discovered by the Herschel-Astrophysical Terahertz Large Area Survey (H-ATLAS). We present 0.″2−0.″4 resolution images of the rest-frame 188 and 419 μm dust continuum and the CO(6–5), H2O(211−202), and Jup = 2 H2O+ line emission. We also report the detection of H2O(211−202) in this source. The dust continuum and molecular gas emission are resolved into a nearly complete ∼1.″5 diameter Einstein ring plus a weaker image in the center, which is caused by a special dual deflector lensing configuration. The observed line profiles of the CO(6–5), H2O(211−202), and Jup = 2 H2O+ lines are strikingly similar. In the source plane, we reconstruct the dust continuum images and the spectral cubes of the CO, H2O, and H2O+ line emission at sub-kiloparsec scales. The reconstructed dust emission in the source plane is dominated by a compact disk with an effective radius of 0.7 ± 0.1 kpc plus an overlapping extended disk with a radius twice as large. While the average magnification for the dust continuum is μ ∼ 10−11, the magnification of the line emission varies from 5 to 22 across different velocity components. The line emission of CO(6–5), H2O(211−202), and H2O+ have similar spatial and kinematic distributions. The molecular gas and dust content reveal that G09v1.97 is a gas-rich major merger in its pre-coalescence phase, with a total molecular gas mass of ∼1011 M⊙. Both of the merging companions are intrinsically ultra-luminous infrared galaxies (ULIRGs) with infrared luminosities LIR reaching ≳4 × 1012 L⊙, and the total LIR of G09v1.97 is (1.4 ± 0.7)×1013 L⊙. The approaching southern galaxy (dominating from V = −400 to −150 km s−1 relative to the systemic velocity) shows no obvious kinematic structure with a semi-major half-light radius of as = 0.4 kpc, while the receding galaxy (0 to 350 km s−1) resembles an as = 1.2 kpc rotating disk. The two galaxies are separated by a projected distance of 1.3 kpc, bridged by weak line emission (−150 to 0 km s−1) that is co-spatially located with the cold dust emission peak, suggesting a large amount of cold interstellar medium (ISM) in the interacting region. As one of the most luminous star-forming dusty high-redshift galaxies, G09v1.97 is an exceptional source for understanding the ISM in gas-rich starbursting major merging systems at high redshift.

Search for Light Resonances Decaying into Pairs of Muons as a Signal of New Physics

Abstract: A search for groups of collimated muons is performed using a data sample collected by the CMS experiment at the LHC, at a centre-of-mass energy of 7 TeV, and corresponding to an integrated luminosity of 35 inverse picobarns. The analysis searches for production of new low-mass states decaying into pairs of muons and is designed to achieve high sensitivity to a broad range of models predicting leptonic jet signatures. With no excess observed over the background expectation, upper limits on the production cross section times branching fraction times acceptance are set, ranging from 0.1 to 0.5 pb at the 95% CL depending on event topology. In addition, the results are interpreted in several benchmark models in the context of supersymmetry with a new light dark sector exploring previously inaccessible parameter space.

Measurements of integrated and differential cross sections for isolated photon pair production in pp collisions at √s=8 TeV with the ATLAS detector

Abstract: A measurement of the production cross section for two isolated photons in proton-proton collisions at a center-of-mass energy of root s = 8 TeV is presented. The results are based on an integrated ...

Measurement of jet shapes in top-quark pair events at √s = 7 TeV using the ATLAS detector

Abstract: A measurement of jet shapes in top-quark pair events using 1.8 fb(-1) of pp collision data recorded by the ATLAS detector at the LHC is presented. Samples of top-quark pair events are selected in b ...

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.).