Brad Abbott

Bio: Brad Abbott is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 137, co-authored 1566 publications receiving 98604 citations. Previous affiliations of Brad Abbott include Aix-Marseille University & Purdue University.

Search for W ' boson resonances decaying to a top quark and a bottom quark

Abstract: We search for the production of a heavy W ' gauge boson that decays to third generation quarks in 0: 9 fb(-1) of p (p) over bar collisions at root s = 1: 96 TeV, collected with the D0 detector at the Fermilab Tevatron collider. We find no significant excess in the final-state invariant mass distribution and set upper limits on the production cross section times branching fraction. For a left-handed W ' boson with SM couplings, we set a lower mass limit of 731 GeV. For right-handed W ' bosons, we set lower mass limits of 739 GeV if the W ' boson decays to both leptons and quarks and 768 GeV if the W ' boson decays only to quarks. We also set limits on the coupling of the W ' boson to fermions as a function of its mass.

Measurement of γ+b+X and γ+c+X production cross sections in pp̄ collisions at s=1.96TeV

Abstract: First measurements of the differential cross sections for the inclusive production of a photon in association with a heavy quark (c, b) jet are presented, covering photon transverse momenta 30-150 GeV, photon rapidities | y_gamma| 15 GeV. The results are based on an integrated luminosity of 1 fb^-1 in ppbar collisions at sqrt(s)=1.96 TeV recorded with the D0 detector at the Fermilab Tevatron Collider. The results are compared with next-to-leading order perturbative QCD predictions.

Search for dark matter produced in association with a Higgs boson decaying to two bottom quarks in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: This article reports on a search for dark matter pair production in association with a Higgs boson decaying to a pair of bottom quarks, using data from 20.3 fb(-1) of pp collisions at a center-of-m ...

Measurement of flow harmonics with multi-particle cumulants in Pb+Pb collisions at√sNN = 2.76 TeV with the ATLAS detector

Abstract: ATLAS measurements of the azimuthal anisotropy in lead-lead collisions at root(NN)-N-S = 2.76 TeV are shown using a dataset of approximately 7 mu b(-1) collected at the LHC in 2010. The measurements are performed for charged particles with transverse momenta 0.5 < p(T) < 20 GeV and in the pseudorapidity range vertical bar eta vertical bar < 2.5. The anisotropy is characterized by the Fourier coefficients, vn, of the charged-particle azimuthal angle distribution for n = 2-4. The Fourier coefficients are evaluated using multi-particle cumulants calculated with the generating function method. Results on the transverse momentum, pseudorapidity and centrality dependence of the v(n) coefficients are presented. The elliptic flow, v(2), is obtained from the two-, four-, six-and eight-particle cumulants while higher-order coefficients, v(3) and v(4), are determined with two-and four-particle cumulants. Flow harmonics v(n) measured with four-particle cumulants are significantly reduced compared to the measurement involving two-particle cumulants. A comparison to v(n) measurements obtained using different analysis methods and previously reported by the LHC experiments is also shown. Results of measurements of flow fluctuations evaluated with multiparticle cumulants are shown as a function of transverse momentum and the collision centrality. Models of the initial spatial geometry and its fluctuations fail to describe the flow fluctuations measurements.

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 …

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.).

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These