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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
TL;DR: In this paper, the authors reported a measurement of the B-s(0) lifetime in the semileptonic decay channel using approximately 0.4 fb(-1) of data collected with the D0 detector during 2002-2004.
Abstract: We report a measurement of the B-s(0) lifetime in the semileptonic decay channel B-s(0)-> D-s(-)mu(+)nu X (and its charge conjugate), using approximately 0.4 fb(-1) of data collected with the D0 detector during 2002-2004. Using 5176 reconstructed D-s(-)mu(+) signal events, we have measured the B-s(0) lifetime to be tau(B-s(0))=1.398 +/- 0.044(stat)(-0.025)(+0.028)(syst) ps. This is the most precise measurement of the B-s(0) lifetime to date.

25 citations

Journal ArticleDOI
Georges Aad, Brad Abbott1, J. Abdallah2, S. Abdel Khalek3  +3026 moreInstitutions (175)
TL;DR: In this paper, a measurement of tau polarization in W->taunu decays is presented, measured from the energies of the decay products in hadronic tau decays with a single final state charged particle.
Abstract: In this paper, a measurement of tau polarization in W->taunu decays is presented. It is measured from the energies of the decay products in hadronic tau decays with a single final state charged particle. The data, corresponding to an integrated luminosity of 24 pb^-1, were collected by the ATLAS experiment at the Large Hadron Collider in 2010. The measured value of the tau polarization is Ptau = -1.06 +/- 0.04 (stat) + 0.05 (syst) - 0.07 (syst), in agreement with the Standard Model prediction, and is consistent with a physically allowed 95% CL interval [-1,-0.91]. Measurements of tau polarization have not previously been made at hadron colliders.

25 citations

Journal ArticleDOI
Brad Abbott1, M. Abolins2, V.V. Abramov, Bobby Samir Acharya3  +360 moreInstitutions (32)
TL;DR: In this paper, a neural network analysis yields a cross-section of $7.1\ifmmode\pm\else\textpm\fi{}1.8(\mathrm{stat})\ifmmODE\pm,
Abstract: We present a measurement of $t\overline{t}$ production in $p\overline{p}$ collisions at $\sqrt{s}\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}1.8\mathrm{TeV}$ from $110{\mathrm{pb}}^{\ensuremath{-}1}$ of data collected in the all-jets decay channel with the D0 detector at Fermilab. A neural network analysis yields a cross section of $7.1\ifmmode\pm\else\textpm\fi{}2.8(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.5(\mathrm{syst})\mathrm{pb}$ at a top quark mass $({m}_{t})$ of $172.1\mathrm{GeV}{/c}^{2}$. Using previous D0 measurements from dilepton and single lepton channels, the combined D0 result for the $t\overline{t}$ production cross section is $5.9\ifmmode\pm\else\textpm\fi{}1.2(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.1(\mathrm{syst})\mathrm{pb}$ for ${m}_{t}\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}172.1\phantom{\rule{0ex}{0ex}}\mathrm{GeV}{/c}^{2}$.

25 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, Ovsat Abdinov4  +2840 moreInstitutions (188)
TL;DR: In this article, a search for charged heavy long-lived particles with the ATLAS detector at the Large Hadron Collider is presented, based on a data sample corresponding to an integrated luminosity of 18.4 fb(-1) of pp collisions at root s = 8 TeV.
Abstract: Many extensions of the Standard Model predict the existence of charged heavy long-lived particles, such as R-hadrons or charginos. These particles, if produced at the Large Hadron Collider, should bemoving non-relativistically and are therefore identifiable through the measurement of an anomalously large specific energy loss in the ATLAS pixel detector. Measuring heavy long-lived particles through their track parameters in the vicinity of the interaction vertex provides sensitivity to metastable particles with lifetimes from 0.6 ns to 30 ns. A search for such particles with the ATLAS detector at the Large Hadron Collider is presented, based on a data sample corresponding to an integrated luminosity of 18.4 fb(-1) of pp collisions at root s = 8 TeV. No significant deviation from the Standard Model background expectation is observed, and lifetime-dependent upper limits on R-hadrons and chargino production are set. Gluino R-hadrons with 10 ns lifetime and masses up to 1185 GeV are excluded at 95 % confidence level, and so are charginos with 15 ns lifetime and masses up to 482 GeV.

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