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.

Tevatron Combination of Single-Top-Quark Cross Sections and Determination of the Magnitude of the Cabibbo-Kobayashi-Maskawa Matrix Element Vtb

Abstract: We present the final combination of CDF and D0 measurements of cross sections for single-top-quark production in proton-antiproton collisions at a center-of-mass energy of 196 TeV The data correspond to total integrated luminosities of up to 97 fb(-1) per experiment The t-channel cross section is measured to be sigma(t) = 225(-031)(+029) pb We also present the combinations of the two-dimensional measurements of the s- vs t-channel cross section In addition, we give the combination of the s + t channel cross section measurement resulting in sigma(s+t) = 330(-040)(+052) pb, without assuming the standard model value for the ratio sigma(s)/sigma(t) The resulting value of the magnitude of the top-to-bottom quark coupling is vertical bar V-tb vertical bar = 102(-005)(+006), corresponding to vertical bar V-tb vertical bar > 092 at the 95% C L

Measurement of the lifetime of the Bc± meson in the semileptonic decay channel

Abstract: Using approximately 1.3 fb(-1) of data collected by the D0 detector between 2002 and 2006, we measure the lifetime of the B-c(+/-) meson in the B-c(+/-)-> J/psi mu(+/-)+X final state. A simultaneous unbinned likelihood fit to the J/psi+mu invariant mass and lifetime distributions yields a signal of 881 +/- 80(stat) candidates and a lifetime measurement of tau(B-c(+/-))=0.448(-0.036)(+0.038)(stat)+/- 0.032(syst) ps.

Precision Measurement of the Ratio B(t→Wb)/B(t→Wq) and Extraction of Vtb

Abstract: We present a measurement of the ratio of top quark branching fractions R = B(t -> Wb)/B(t -> Wq), where q can be a d, s or b quark, in the lepton+jets and dilepton ttbar final states. The measurement uses data from 5.4 fb-1 of ppbar collisions collected with the D0 detector at the Fermilab Tevatron Collider. We measure R = 0.90 +/- 0.04, and we extract the CKM matrix element |Vtb| as |Vtb| = 0.95 +/- 0.02, assuming unitarity of the 3x3 CKM matrix.

Observation of Centrality-Dependent Acoplanarity for Muon Pairs Produced via Two-Photon Scattering in Pb+Pb Collisions at sNN =5.02 TeV with the ATLAS Detector

Abstract: This Letter presents a measurement of γγ→μ^{+}μ^{-} production in Pb+Pb collisions recorded by the ATLAS detector at the Large Hadron Collider at sqrt[s_{NN}]=5.02 TeV with an integrated luminosity of 0.49 nb^{-1}. The azimuthal angle and transverse momentum correlations between the muons are measured as a function of collision centrality. The muon pairs are produced from γγ through the interaction of the large electromagnetic fields of the nuclei. The contribution from background sources of muon pairs is removed using a template fit method. In peripheral collisions, the muons exhibit a strong back-to-back correlation consistent with previous measurements of muon pair production in ultraperipheral collisions. The angular correlations are observed to broaden significantly in central collisions. The modifications are qualitatively consistent with rescattering of the muons while passing through the hot matter produced in the collision.

Measurement of the top quark mass in the tt¯ → lepton+jets channel from √s = 8 TeV ATLAS data and combination with previous results

Abstract: The top quark mass is measured using a template method in the ttlepton+jets channel (lepton is e or ) using ATLAS data recorded in 2012 at the LHC. The data were taken at a proton-proton centre-of- ...

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