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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.


Papers
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Journal ArticleDOI
V. M. Abazov1, Brad Abbott2, M. Abolins3, B. S. Acharya4  +550 moreInstitutions (83)
TL;DR: In this paper, a study of mu mu mu, eeee, and mu mu ee events using 1 fb(-1) of data collected with the D0 detector at the Fermilab Tevatron p (p) over bar Collider at root s = 1.96 TeV.
Abstract: We present a study of mu mu mu mu, eeee, and mu mu ee events using 1 fb(-1) of data collected with the D0 detector at the Fermilab Tevatron p (p) over bar Collider at root s = 1.96 TeV. Requiring t ...

25 citations

Journal ArticleDOI
V. M. Abazov1, Brad Abbott2, M. Abolins3, B. S. Acharya4  +560 moreInstitutions (76)
TL;DR: The study of the flavor-changing-neutral-current process c-->u micro(+) micro(-) using 1.3 fb(-1) of pp[over ] collisions at square root s = 1.96 TeV recorded by the D0 detector operating at the Fermilab Tevatron Collider sees clear indications of the charged-current mediated D(s)(+) and D(+)-->phipi(+) -->micro(+)micro(-)pi(-) final states
Abstract: We study the flavor-changing-neutral-current process c -> u mu(+)mu(-) using 1.3 fb(-1) of p (p) over bar collisions at root s = 1.96 TeV recorded by the D0 detector operating at the Fermilab Tevatron Collider. We see clear indications of the charged-current mediated D-s(+) and D+->phi pi(+)->mu(+)mu(-)pi(+) final states with significance greater than 4 standard deviations above background for the D+ state. We search for the continuum neutral-current decay of D+->pi(+)mu(+)mu(-) in the dimuon invariant mass spectrum away from the phi resonance. We see no evidence of signal above background and set a limit of B(D+->pi(+)mu(+)mu(-)) u mu(+)mu(-) transition.

25 citations

Journal ArticleDOI
V. M. Abazov1, Brad Abbott2, M. Abolins3, B. S. Acharya4  +510 moreInstitutions (77)
01 Jan 2007
TL;DR: In this paper, a search for pair production of the lightest supersymmetric partner of the top quark was performed in the lepton+jets channel using 0.9 fb-1 of data collected by the D0 experiment.
Abstract: A search for pair production of the lightest supersymmetric partner of the top quark is performed in the lepton+jets channel using 0.9 fb-1 of data collected by the D0 experiment. Kinematic differences between scalar top quark pair production and the dominant top quark pair production background are used to separate the two processes. First limits from Run II of the Fermilab Tevatron Collider for the scalar top quark decaying to a chargino and a b quark are obtained for scalar top quark masses of 130-190 GeV and chargino masses of 90-150 GeV.

25 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Ovsat Abdinov3  +2979 moreInstitutions (213)
TL;DR: A normalized differential cross-section measurement in a fiducial phase-space region where interference effects between top-quark pair production and associated production of a single top quark with a W boson and a b-quarks are significant is presented.
Abstract: This Letter presents a normalized differential cross-section measurement in a fiducial phase-space region where interference effects between top-quark pair production and associated production of a single top quark with a W boson and a b-quark are significant. Events with exactly two leptons (ee, μμ, or eμ) and two b-tagged jets that satisfy a multiparticle invariant mass requirement are selected from 36.1 fb^{-1} of proton-proton collision data taken at sqrt[s]=13 TeV with the ATLAS detector at the LHC in 2015 and 2016. The results are compared with predictions from simulations using various strategies for the interference. The standard prescriptions for interference modeling are significantly different from each other but are within 2σ of the data. State-of-the-art predictions that naturally incorporate interference effects provide the best description of the data in the measured region of phase space most sensitive to these effects. These results provide an important constraint on interference models and will guide future model development and tuning.

25 citations

Journal ArticleDOI
V. M. Abazov1, Brad Abbott2, M. Abolins3, B. S. Acharya4  +523 moreInstitutions (87)
TL;DR: Limits are set on right-handed vector couplings as well as left-handed and right- handed tensor couplings based on about 1 fb(-1) of data collected by the D0 experiment.
Abstract: Anomalous Wtb couplings modify the angular correlations of the top-quark decay products and change the single top-quark production cross section. We present limits on anomalous top-quark couplings by combining information from W boson helicity measurements in top-quark decays and anomalous coupling searches in the single top-quark final state. We set limits on right-handed vector couplings as well as left-handed and right-handed tensor couplings based on about 1 fb(-1) of data collected by the D0 experiment.

25 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
Claude Amsler1, Michael Doser2, Mario Antonelli, D. M. Asner3  +173 moreInstitutions (86)
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.

12,798 citations

Journal ArticleDOI
01 Apr 1988-Nature
TL;DR: In this paper, a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) is presented.
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

9,929 citations