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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, Jalal Abdallah3, Ovsat Abdinov4  +2853 moreInstitutions (191)
TL;DR: In this article, the authors proposed a method to find the optimal set of features for each node in a set of images, which can be found under doi:10.1140/epjc/s10052-016-3910-6
Abstract: The online version of the original article can be found under doi:10.1140/epjc/s10052-016-3910-6

15 citations

Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Samuel Webb3, Timo Dreyer4  +2997 moreInstitutions (217)
TL;DR: In this paper, single top-quark production in association with a Z boson is measured in the trilepton channel, where events containing three isolated charged leptons (electrons or muons) and two or three jets, one of which is identified as containing a b-hadron, are selected.
Abstract: Single top-quark production in association with a Z boson, where the Z boson decays to a pair of charged leptons, is measured in the trilepton channel. The proton-proton collision data collected by the ATLAS experiment from 2015 to 2018 at a centre-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of 139 fb$^{−1}$. Events containing three isolated charged leptons (electrons or muons) and two or three jets, one of which is identified as containing a b-hadron, are selected. The main backgrounds are from $ t\overline{t}Z $ and diboson production. Neural networks are used to improve the background rejection and extract the signal. The measured cross-section for tl$^{+}$l$^{−}$q production, including non-resonant dilepton pairs with $ {m}_{{\mathrm{\ell}}^{+}{\mathrm{\ell}}^{-}} $> 30 GeV, is 97 ± 13 (stat.) ± 7 (syst.) fb, consistent with the Standard Model prediction.[graphic not available: see fulltext]

15 citations

Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Samuel Webb3, Timo Dreyer4  +2970 moreInstitutions (168)
TL;DR: In this paper, the production cross-sections for W and Z bosons are measured using ATLAS data corresponding to an integrated luminosity of 4.76$-TeV, where the decay channels can be an electron or a muon.
Abstract: The production cross-sections for $W^{\pm }$ and Z bosons are measured using ATLAS data corresponding to an integrated luminosity of 4.0 pb$^{-1}$ collected at a centre-of-mass energy $\sqrt{s}=2.76$ TeV. The decay channels $W \rightarrow \ell u $ and $Z \rightarrow \ell \ell $ are used, where $\ell $ can be an electron or a muon. The cross-sections are presented for a fiducial region defined by the detector acceptance and are also extrapolated to the full phase space for the total inclusive production cross-section. The combined (average) total inclusive cross-sections for the electron and muon channels are: $\begin{aligned} \sigma ^{\text {tot}}_{W^{+}\rightarrow \ell u }= & {} 2312 \pm 26\ (\text {stat.})\\&\pm 27\ (\text {syst.}) \pm 72\ (\text {lumi.}) \pm 30\ (\text {extr.})~\text {pb} , \\ \sigma ^{\text {tot}}_{W^{-}\rightarrow \ell u }= & {} 1399 \pm 21\ (\text {stat.})\ \pm 17\ (\text {syst.}) \\&\pm 43\ (\text {lumi.}) \pm 21\ (\text {extr.})~\text {pb} , \\ \sigma ^{\text {tot}}_{Z \rightarrow \ell \ell }= & {} 323.4 \pm 9.8\ (\text {stat.}) \pm 5.0\ (\text {syst.})\\&\pm 10.0\ (\text {lumi.}) \pm 5.5 (\text {extr.}) ~\text {pb} . \end{aligned}$Measured ratios and asymmetries constructed using these cross-sections are also presented. These observables benefit from full or partial cancellation of many systematic uncertainties that are correlated between the different measurements.

15 citations

01 Jan 2011
TL;DR: In this paper, a measurement of the production cross section of top quark pairs in proton-proton (pp) collisions at √s = 7 TeV recorded with the ATLAS detector at the CERN Large Hadron was presented.
Abstract: A measurement is presented of the production cross section of top quark pairs (σ_{\ttbar{}}) in proton-proton (pp) collisions at √s = 7 TeV recorded with the ATLAS detector at the CERN Large Hadron ...

14 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Ovsat Abdinov3  +2965 moreInstitutions (215)
TL;DR: In this article, the production of muons from heavy-flavor decays in root s(NN) = 2.76 TeV Pb+Pb collisions at the LHC were analyzed.
Abstract: ATLAS measurements of the production of muons from heavy-flavor decays in root s(NN) = 2.76 TeV Pb+Pb collisions and root s = 2.76 TeV pp collisions at the LHC are presented. Integrated luminositie ...

14 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