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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
Georges Aad1, Brad Abbott2, J. Abdallah3, A. A. Abdelalim4  +3040 moreInstitutions (194)
TL;DR: In this paper, an update of a search for supersymmetry in final states containing jets, missing transverse momentum, and one isolated electron or muon is presented, using 1.04 fb(-1) of proton-proton collision data at root s =7 TeV recorded by the ATLAS experiment at the LHC in the first half of 2011.
Abstract: We present an update of a search for supersymmetry in final states containing jets, missing transverse momentum, and one isolated electron or muon, using 1.04 fb(-1) of proton-proton collision data at root s =7 TeV recorded by the ATLAS experiment at the LHC in the first half of 2011. The analysis is carried out in four distinct signal regions with either three or four jets and variations on the (missing) transverse momentum cuts, resulting in optimized limits for various supersymmetry models. No excess above the standard model background expectation is observed. Limits are set on the visible cross section of new physics within the kinematic requirements of the search. The results are interpreted as limits on the parameters of the minimal supergravity framework, limits on cross sections of simplified models with specific squark and gluino decay modes, and limits on parameters of a model with bilinear R-parity violation.

142 citations

Journal ArticleDOI
Morad Aaboud, Alexander Kupco1, Samuel Webb2, Timo Dreyer3  +2969 moreInstitutions (195)
TL;DR: Algorithms used for the reconstruction and identification of electrons in the central region of the ATLAS detector at the Large Hadron Collider (LHC) are presented in this article, these algorithms a...
Abstract: Algorithms used for the reconstruction and identification of electrons in the central region of the ATLAS detector at the Large Hadron Collider (LHC) are presented in this paper; these algorithms a ...

140 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, S. Abdel Khalek4  +2917 moreInstitutions (211)
TL;DR: In this article, the results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1 fb(-1) of proton-proton collision data at = 8 TeV recorded with the ATLAS detector at the LHC are reported.
Abstract: The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1 fb(-1) of proton-proton collision data at = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via or , where denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of . For a branching fraction of 100%, top squark masses in the range 270-645 GeV are excluded for masses below 30 GeV. For a branching fraction of 50% to either or , and assuming the mass to be twice the mass, top squark masses in the range 250-550 GeV are excluded for masses below 60 GeV.

140 citations

Journal ArticleDOI
TL;DR: In this paper, the mass and angular distributions of dijets produced in LHC proton-proton collisions at a centre-of-mass energy root s = 7TeV have been studied with the ATLAS detector using the full 2011 data set with an integrated luminosity of 4.8 fb(-1).
Abstract: Mass and angular distributions of dijets produced in LHC proton-proton collisions at a centre-of-mass energy root s = 7TeV have been studied with the ATLAS detector using the full 2011 data set with an integrated luminosity of 4.8 fb(-1). Dijet masses up to similar to 4.0TeV have been probed. No resonance-like features have been observed in the dijet mass spectrum, and all angular distributions are consistent with the predictions of QCD. Exclusion limits on six hypotheses of new phenomena have been set at 95% CL in terms of mass or energy scale, as appropriate. These hypotheses include excited quarks below 2.83 TeV, colour octet scalars below 1.86TeV, heavy W bosons below 1.68 TeV, string resonances below 3.61 TeV, quantum black holes with six extra space-time dimensions for quantum gravity scales below 4.11 TeV, and quark contact interactions below a compositeness scale of 7.6 TeV in a destructive interference scenario.

139 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Dale Charles Abbott3, A. Abed Abud4, Kira Abeling5, Deshan Kavishka Abhayasinghe6, Syed Haider Abidi7, Ossama AbouZeid8, N. L. Abraham9, Halina Abramowicz10, Henso Abreu11, Yiming Abulaiti12, Bobby Samir Acharya13, Bobby Samir Acharya14, Baida Achkar5, Shunsuke Adachi15, Lennart Adam16, C. Adam Bourdarios17, Leszek Adamczyk18, Lukas Adamek7, Jahred Adelman19, Michael Adersberger20, Aytul Adiguzel21, Sofia Adorni22, Tim Adye23, A. A. Affolder24, Yoav Afik11, Christina Agapopoulou25, Merve Nazlim Agaras26, A. Aggarwal27, Catalin Agheorghiesei28, J. A. Aguilar-Saavedra29, J. A. Aguilar-Saavedra30, Faig Ahmadov31, Waleed Syed Ahmed32, Xiaocong Ai33, Giulio Aielli34, Shunichi Akatsuka35, T. P. A. Åkesson, Ece Akilli22, A. V. Akimov36, K. Al Khoury25, Gian Luigi Alberghi37, J. Albert38, M. J. Alconada Verzini10, Sara Caroline Alderweireldt39, Martin Aleksa39, Igor Aleksandrov31, Calin Alexa, Theodoros Alexopoulos40, Alice Alfonsi41, Fabrizio Alfonsi37, Muhammad Alhroob2, Babar Ali42, Malik Aliev43, Gianluca Alimonti, Steven Patrick Alkire44, Corentin Allaire25, Bmm Allbrooke9, Benjamin William Allen45, Philip Patrick Allport46, Alberto Aloisio, Alejandro Alonso47, Francisco Alonso48, Cristiano Alpigiani44, Azzah Aziz Alshehri49, M. Alvarez Estevez50, D. Álvarez Piqueras29, M. G. Alviggi, Y. Amaral Coutinho51, Alessandro Ambler32, Luca Ambroz52, Christoph Amelung53, D. Amidei54, S. P. Amor Dos Santos, Simone Amoroso, Cherifa Sabrina Amrouche22, Fenfen An55, Christos Anastopoulos56, Nansi Andari, Timothy Andeen57, Christoph Falk Anders58, John Kenneth Anders59, A. Andreazza60, Andrei58, Christopher Anelli38, Stylianos Angelidakis26, Aaron Angerami61, Alexey Anisenkov62, Alexey Anisenkov63, Alberto Annovi, Claire Antel22, Matthew Thomas Anthony56, Egor Antipov64, Massimo Antonelli, D. J. A. Antrim65, F. Anulli, Masato Aoki66, J. A. Aparisi Pozo29, L. Aperio Bella67, Juan Pedro Araque, Araujo Ferraz51, R. Araujo Pereira51 
Aix-Marseille University1, University of Oklahoma2, University of Massachusetts Amherst3, University of Pavia4, University of Göttingen5, Royal Holloway, University of London6, University of Toronto7, Niels Bohr Institute8, University of Sussex9, Tel Aviv University10, Technion – Israel Institute of Technology11, Argonne National Laboratory12, International Centre for Theoretical Physics13, King's College London14, University of Tokyo15, University of Mainz16, University of Savoy17, AGH University of Science and Technology18, Northern Illinois University19, Ludwig Maximilian University of Munich20, Boğaziçi University21, University of Geneva22, Rutherford Appleton Laboratory23, Santa Cruz Institute for Particle Physics24, Université Paris-Saclay25, University of Auvergne26, Radboud University Nijmegen27, Alexandru Ioan Cuza University28, Spanish National Research Council29, University of Granada30, Joint Institute for Nuclear Research31, McGill University32, Lawrence Berkeley National Laboratory33, University of Rome Tor Vergata34, Kyoto University35, Russian Academy of Sciences36, University of Bologna37, University of Victoria38, CERN39, National Technical University of Athens40, University of Amsterdam41, Czech Technical University in Prague42, Tomsk State University43, University of Washington44, University of Oregon45, University of Birmingham46, University of Copenhagen47, National University of La Plata48, University of Glasgow49, Autonomous University of Madrid50, Federal University of Rio de Janeiro51, University of Oxford52, Brandeis University53, University of Michigan54, Iowa State University55, University of Sheffield56, University of Texas at Austin57, Heidelberg University58, University of Bern59, University of Milan60, Columbia University61, Novosibirsk State University62, Budker Institute of Nuclear Physics63, Oklahoma State University–Stillwater64, University of California, Irvine65, KEK66, Chinese Academy of Sciences67
TL;DR: In this article, a search for new resonances decaying into a pair of jets is reported using the dataset of proton-proton collisions recorded at s = 13 TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018.
Abstract: A search for new resonances decaying into a pair of jets is reported using the dataset of proton-proton collisions recorded at s = 13 TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139 fb−1. The distribution of the invariant mass of the two leading jets is examined for local excesses above a data-derived estimate of the Standard Model background. In addition to an inclusive dijet search, events with jets identified as containing b-hadrons are examined specifically. No significant excess of events above the smoothly falling background spectra is observed. The results are used to set cross-section upper limits at 95% confidence level on a range of new physics scenarios. Model-independent limits on Gaussian-shaped signals are also reported. The analysis looking at jets containing b-hadrons benefits from improvements in the jet flavour identification at high transverse momentum, which increases its sensitivity relative to the previous analysis beyond that expected from the higher integrated luminosity.

138 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