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.

Discovery of four apparently cold dusty galaxies at z=3.62-5.85 in the COSMOS field: direct evidence of CMB impact on high-redshift galaxy observables

Abstract: We report Atacama Large Millimetre Array (ALMA) observations of four high-redshift dusty star-forming galaxy candidates selected from far-Infrared (FIR)/submm observations in the COSMOS field. We securely detect all galaxies in the continuum and spectroscopically confirm them at z=3.62--5.85 using ALMA 3mm line scans, detecting multiple CO and/or [CI] transitions. This includes the most distant dusty galaxy currently known in the COSMOS field, ID85001929 at z=5.847. These redshifts are lower than we had expected as these galaxies have substantially colder dust temperatures (i.e., their SEDs peak at longer rest frame wavelengths) than most literature sources at z>4. The observed cold dust temperatures are best understood as evidence for optically thick dust continuum in the FIR, rather than the result of low star formation efficiency with rapid metal enrichment. We provide direct evidence that, given their cold spectral energy distributions, CMB plays a significant role biasing their observed Rayleigh-Jeans (RJ) slopes to unlikely steep values and, possibly, reducing their CO fluxes by a factor of two. We recover standard RJ slopes when the CMB contribution is taken into account. High resolution ALMA imaging shows compact morphology and evidence for mergers. This work reveals a population of cold dusty star-forming galaxies that were under-represented in current surveys, and are even colder than typical Main Sequence galaxies at the same redshift. High FIR dust optical depth might be a widespread feature of compact starbursts at any redshift.

Search for Pair Production of Third-Generation Leptoquarks and Top Squarks in pp Collisions at √s=7 TeV

Abstract: Results are presented from a search for the pair production of third-generation scalar and vector leptoquarks, as well as for top squarks in R-parity-violating supersymmetric models. In either scenario, the new, heavy particle decays into a tau lepton and a b quark. The search is based on a data sample of pp collisions at sqrt(s) = 7 TeV, which is collected by the CMS detector at the LHC and corresponds to an integrated luminosity of 4.8 inverse femtobarns. The number of observed events is found to be in agreement with the standard model prediction, and exclusion limits on mass parameters are obtained at the 95% confidence level. Vector leptoquarks with masses below 760 GeV are excluded and, if the branching fraction of the scalar leptoquark decay to a tau lepton and a b quark is assumed to be unity, third-generation scalar leptoquarks with masses below 525 GeV are ruled out. Top squarks with masses below 453 GeV are excluded for a typical benchmark scenario, and limits on the coupling between the top squark, tau lepton, and b quark, lambda'[333] are obtained. These results are the most stringent for these scenarios to date.

Measurements of jet multiplicity and differential production cross sections of Z+jets events in proton-proton collisions at s =7TeV

Abstract: Measurements of differential cross sections are presented for the production of a Z boson and at least one hadronic jet in proton-proton collisions at √s=7 TeV, recorded by the CMS detector, using a data sample corresponding to an integrated luminosity of 4.9 fb^(−1). The jet multiplicity distribution is measured for up to six jets. The differential cross sections are measured as a function of jet transverse momentum and pseudorapidity for the four highest transverse momentum jets. The distribution of the scalar sum of jet transverse momenta is also measured as a function of the jet multiplicity. The measurements are compared with theoretical predictions at leading and next-to-leading order in perturbative QCD.

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.).