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 a fourth generation t′ quark in pp̄ collisions at √s=1.96TeV

Abstract: We present a search for pair production of a fourth generation t' quark and its antiparticle, followed by their decays to a W boson and a jet, based on an integrated luminosity of 5.3/fb of proton-antiproton collisions at sqrt{s}=1.96 TeV collected by the D0 Collaboration at the Fermilab Tevatron Collider. We set upper limits on the t't'bar production cross section that exclude at the 95% C.L. a t' quark that decays exclusively to W+jet with a mass below 285 GeV. We observe a small excess in the muon+jets channel which reduces the mass range excluded compared to the expected limit of 320 GeV in the absence of a signal.

First study of the radiation-amplitude zero in Wγ production and limits on anomalous WWγ couplings at s=1.96TeV

Abstract: We present results from a study of p (p) over bar -> W gamma+X events utilizing data corresponding to 0.7 fb(-1) of integrated luminosity at root s = 1.96 TeV collected by the D0 detector at the Fermilab Tevatron Collider. We set limits on anomalous WW gamma couplings at the 95% C.L. The one-dimensional 95% C.L. limits are 0.49

Measurement of the W boson helicity in top quark decays at D0

Abstract: The authors present a measurement of the fraction f{sub +} of right-handed W bosons produced in top quark decays, based on a candidate sample of t{bar t} events in the {ell}+jets and dilepton decay channels corresponding to an integrated luminosity of 370 pb{sup -1} collected by the D0 detector at the Fermilab Tevatron p{bar p} Collider at {radical}s = 1.96 TeV. They reconstruct the decay angle {theta}* for each lepton. By comparing the cos{theta}* distribution from the data with those for the expected background and signal for various values of f{sub +}, they find f{sub +} = 0.056 {+-} 0.080(stat) {+-} 0.057(syst). (f{sub +} < 0.23 at 95% C.L.), consistent with the standard model prediction of f{sub +} = 3.6 x 10{sup -4}.

Search for excited electrons in pp̄ collisions at s=1.96TeV

Abstract: We present the results of a search for the production of an excited state of the electron, e(*), in proton-antiproton collisions at root s = 1.96 TeV. The data were collected with the D0 experiment at the Fermilab Tevatron Collider and correspond to an integrated luminosity of approximately 1 fb(-1). We search for e(*) in the process p (p) over bar -> e(*)e, with the e(*) subsequently decaying to an electron plus photon. No excess above the standard model background is observed. Interpreting our data in the context of a model that describes e(*) production by four-fermion contact interactions and e(*) decay via electroweak processes, we set 95% C.L. upper limits on the production cross section ranging from 8.9 to 27 fb, depending on the mass of the excited electron. Choosing the scale for contact interactions to be Lambda = 1 TeV, excited electron masses below 756 GeV are excluded at the 95% C.L.

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