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 heavy resonances decaying into WW in the eνμν final state in pp collisions at s=13 TeV with the ATLAS detector.

Abstract: A search for neutral heavy resonances is performed in the WW -> e nu mu nu decay channel using pp collision data corresponding to an integrated luminosity of 36.1 fb(-1), collected at a centre-o ...

Direct measurement of the top quark mass by the DO Collaboration

Abstract: We determine the top quark mass m{sub t} using t{bar t} pairs produced in the DO/ detector by {radical} (s) =1.8thinspTeV p{bar p} collisions in a 125thinsppb{sup {minus}1} exposure at the Fermilab Tevatron. We make a two constraint fit to m{sub t} in t{bar t}{r_arrow}bW{sup +}{bar b}W{sup {minus}} final states with one {ital W} boson decaying to q{bar q} and the other to e{nu} or {mu}{nu}. Likelihood fits to the data yield m{sub t}(l+jets)=173.3{plus_minus}5.6thinsp(stat)thinsp{plus_minus}thinsp5.5thinsp(s st) GeV/c{sup 2}. When this result is combined with an analysis of events in which both {ital W} bosons decay into leptons, we obtain m{sub t}=172.1{plus_minus}5.2thinsp(stat)thinsp{plus_minus}thinsp4.9thinsp(syst) GeV/c{sup 2}. An alternate analysis, using three constraint fits to fixed top quark masses, gives m{sub t}(l+jets)=176.0{plus_minus}7.9thinsp(stat){plus_minus}thinsp4.8thinsp(syst) GeV/c{sup 2}, consistent with the above result. Studies of kinematic distributions of the top quark candidates are also presented. {copyright} {ital 1998} {ital The American Physical Society}

Observation of Light-by-Light Scattering in Ultraperipheral Pb+Pb Collisions with the ATLAS Detector

Abstract: This Letter describes the observation of the light-by-light scattering process, γγ→γγ, in Pb+Pb collisions at sqrt[s_{NN}]=5.02 TeV. The analysis is conducted using a data sample corresponding to an integrated luminosity of 1.73 nb^{-1}, collected in November 2018 by the ATLAS experiment at the LHC. Light-by-light scattering candidates are selected in events with two photons produced exclusively, each with transverse energy E_{T}^{γ}>3 GeV and pseudorapidity |η_{γ}|<2.4, diphoton invariant mass above 6 GeV, and small diphoton transverse momentum and acoplanarity. After applying all selection criteria, 59 candidate events are observed for a background expectation of 12±3 events. The observed excess of events over the expected background has a significance of 8.2 standard deviations. The measured fiducial cross section is 78±13(stat)±7(syst)±3(lumi) nb.

Measurement of the CP-violating phase sJ/ψ using the flavor-tagged decay Bs0→J/ψ in 8fb -1 of pp̄ collisions

Abstract: We report an updated measurement of the CP-violating phase, ϕsJ/ψϕ, and the decay-width difference for the two mass eigenstates, ΔΓs, from the flavor-tagged decay Bs0→J/ψϕ. The data sample correspo ...

Measurement of the charge asymmetry in top quark pair production in pp collisions at √s = 7 TeV using the ATLAS detector

Abstract: A measurement of the top-antitop production charge asymmetry A_C is presented using data corresponding to an integrated luminosity of 1.04 fb^-1 of pp collisions at sqrt(s) = 7 TeV collected by the ATLAS detector at the LHC. Events are selected with a single lepton (electron or muon), missing transverse momentum and at least four jets of which at least one jet is identified as coming from a b-quark. A kinematic fit is used to reconstruct the ttbar event topology. After background subtraction, a Bayesian unfolding procedure is performed to correct for acceptance and detector effects. The measured value of A_C is A_C = -0.018 +/- 0.028 (stat.) +/- 0.023 (syst.), consistent with the prediction from the MC@NLO Monte Carlo generator of A_C = 0.006 +/- 0.002. Measurements of A_C in two ranges of invariant mass of the top-antitop pair is also shown.

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