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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
Morad Aaboud, Alexander Kupco1, Peter Davison2, Samuel Webb3  +2908 moreInstitutions (221)
TL;DR: In this article, measurements of differential cross-sections of top-quark pair production in fiducial phase-spaces are presented as a function of topquark and tt¯ system kinematic observables in proton-proton collisions at a centre-of-mass energy of √s=13 TeV.
Abstract: Measurements of differential cross-sections of top-quark pair production in fiducial phase-spaces are presented as a function of top-quark and tt¯ system kinematic observables in proton-proton collisions at a centre-of-mass energy of √s=13 TeV. The data set corresponds to an integrated luminosity of 3.2 fb−1, recorded in 2015 with the ATLAS detector at the CERN Large Hadron Collider. Events with exactly one electron or muon and at least two jets in the final state are used for the measurement. Two separate selections are applied that each focus on different top-quark momentum regions, referred to as resolved and boosted topologies of the tt¯ final state. The measured spectra are corrected for detector effects and are compared to several Monte Carlo simulations by means of calculated χ2 and p-values.

36 citations

Journal ArticleDOI
S. Abachi1, Brad Abbott2, M. Abolins3, Bobby Samir Acharya4  +387 moreInstitutions (43)
TL;DR: In both pseudorapidity regions the cross section is found to agree with the next-to-leading order QCD prediction for 30≲E T γ ≲80GeV.
Abstract: A measurement of the cross section for production of single, isolated photons is reported for transverse energies in the range of 10–125 GeV for two regions of pseudorapidity, |η| -1 of integrated luminosity accumulated in pp collisions at √s=1.8TeV and recorded with the D0 detector at the Fermilab Tevatron Collider. The background, predominantly from jets which fragment to neutral mesons, is estimated using the longitudinal shower shape in the calorimeter. In both pseudorapidity regions the cross section is found to agree with the next-to-leading order QCD prediction for 30≲E T γ ≲80GeV.

36 citations

Journal ArticleDOI
Morad Aaboud, Alexander Kupco1, Samuel Webb2, Timo Dreyer3  +2989 moreInstitutions (215)
TL;DR: In this article, a search for the pair production of heavy vector-like B quarks was presented, primarily targeting B quark decays into a W boson and a top quark.
Abstract: A search is presented for the pair production of heavy vector-like B quarks, primarily targeting B quark decays into a W boson and a top quark. The search is based on 36.1 fb(-1) of pp collisions a ...

36 citations

Journal ArticleDOI
V. M. Abazov1, Brad Abbott2, Bobby Samir Acharya3, Mark Raymond Adams4  +426 moreInstitutions (83)
TL;DR: In this paper, a measurement of the inclusive jet cross section using the Run II cone algorithm and data collected by the D0 experiment in p (p) over bar collisions at a center-of-mass energy root s = 1.96 TeV, corresponding to an integrated luminosity of 0: 70 fb(-1).
Abstract: We present a measurement of the inclusive jet cross section using the Run II cone algorithm and data collected by the D0 experiment in p (p) over bar collisions at a center-of-mass energy root s = 1.96 TeV, corresponding to an integrated luminosity of 0: 70 fb(-1). The jet energy calibration and the method used to extract the inclusive jet cross section are described. We discuss the main uncertainties, which are dominated by the jet energy scale uncertainty. The results cover jet transverse momenta from 50 GeV to 600 GeV with jet rapidities in the range -2.4 to 2.4 and are compared to predictions using recent proton parton distribution functions. Studies of correlations between systematic uncertainties in transverse momentum and rapidity are presented.

36 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