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Georges Aad

Bio: Georges Aad is an academic researcher from Aix-Marseille University. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 135, co-authored 1121 publications receiving 88811 citations. Previous affiliations of Georges Aad include Centre national de la recherche scientifique & University of Udine.


Papers
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Journal ArticleDOI
Georges Aad1, Brad Abbott2, Dale Charles Abbott3, A. Abed Abud4  +2868 moreInstitutions (221)
TL;DR: In this paper, a new set of proton parton distribution functions, ATLASepWZVjet20, is presented in an analysis at next-to-next-to leading order in QCD.
Abstract: This article presents a new set of proton parton distribution functions, ATLASepWZVjet20, produced in an analysis at next-to-next-to-leading order in QCD. The new data sets considered are the measurements of $W^+$ and $W^-$ boson and $Z$ boson production in association with jets in $pp$ collisions at $\sqrt{s} = 8~\mathrm{TeV}$ performed by the ATLAS experiment at the LHC with integrated luminosities of $20.2~\mathrm{fb}^{-1}$ and $19.9~\mathrm{fb}^{-1}$, respectively. The analysis also considers the ATLAS measurements of differential $W^{\pm}$ and $Z$ boson production at $\sqrt{s} = 7~\mathrm{TeV}$ with an integrated luminosity of $4.6~\mathrm{fb}^{-1}$ and deep-inelastic-scattering data from $e^{\pm}p$ collisions at the HERA accelerator. An improved determination of the sea-quark densities at high Bjorken $x$ is shown, while confirming a strange-quark density similar in size to the up- and down-sea-quark densities in the range $x \lesssim 0.02$ found by previous ATLAS analyses.

18 citations

Journal ArticleDOI
TL;DR: In this article, a dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented, which searches the region just upstream of or inside the muon volume for multi-particle vertices that originate from the decay of particles with long decay paths.
Abstract: A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to b (b) over bar final states, and pp collision data at root s = 7 TeV collected with the ATLAS detector at the LHC during 2011.

17 citations

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, Jalal Abdallah4  +2890 moreInstitutions (168)
TL;DR: In this paper, a measurement of the phi to K+K- production cross section at 7 TeV using pp collision data corresponding to an integrated luminosity of 383 mub-1, collected with the ATLAS experiment at the LHC.
Abstract: A measurement is presented of the phi to K+K- production cross section at sqrt(s) = 7 TeV using pp collision data corresponding to an integrated luminosity of 383 mub-1, collected with the ATLAS experiment at the LHC. Selection of phi(1020) mesons is based on the identification of charged kaons by their energy loss in the pixel detector. The differential cross section is measured as a function of the transverse momentum, pTphi, and rapidity, |yphi|, of the phi(1020) meson in the fiducial region 500 230 MeV and kaon momentum pK< 800 MeV.The integrated phi(1020)-meson production cross section in this fiducial range is measured to be s(phi K+K-) = 570 pm 8 (stat) pm 66 (syst) pm 20 (lumi) mub.

17 citations

Journal ArticleDOI
Morad Aaboud, Alexander Kupco1, Peter Davison2, Samuel Webb3  +2917 moreInstitutions (221)
TL;DR: A search with the ATLAS detector is presented for the Standard Model Higgs boson produced by vector-boson fusion and decaying to a pair of bottom quarks, using 20.2 fb−1 of LHC proton-proton collis...
Abstract: A search with the ATLAS detector is presented for the Standard Model Higgs boson produced by vector-boson fusion and decaying to a pair of bottom quarks, using 20.2 fb−1 of LHC proton-proton collis ...

17 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Jalal Abdallah3  +2895 moreInstitutions (210)
TL;DR: Knowledge of the material in the ATLAS inner tracking detector is crucial in under-standing the reconstruction of charged-particle tracks, the performance of algorithms that identify jets containin... as mentioned in this paper.
Abstract: Knowledge of the material in the ATLAS inner tracking detector is crucial in under-standing the reconstruction of charged-particle tracks, the performance of algorithms that identify jets containin ...

17 citations


Cited by
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[...]

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
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

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, Jalal Abdallah4  +2964 moreInstitutions (200)
TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.

9,282 citations