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.

Measurement of detector-corrected observables sensitive to the anomalous production of events with jets and large missing transverse momentum in pp collisions at √s=13 TeV using the ATLAS detector

Abstract: Observables sensitive to the anomalous production of events containing hadronic jets and missing momentum in the plane transverse to the proton beams at the Large Hadron Collider are presented. The observables are defined as a ratio of cross sections, for events containing jets and large missing transverse momentum to events containing jets and a pair of charged leptons from the decay of a $Z/\gamma ^*$ boson. This definition minimises experimental and theoretical systematic uncertainties in the measurements. This ratio is measured differentially with respect to a number of kinematic properties of the hadronic system in two phase-space regions, one inclusive single-jet region and one region sensitive to vector-boson-fusion topologies. The data are found to be in agreement with the Standard Model predictions and used to constrain a variety of theoretical models for dark-matter production, including simplified models, effective field theory models, and invisible decays of the Higgs boson. The measurements use 3.2 fb$^{-1}$ of proton–proton collision data recorded by the ATLAS experiment at a centre-of-mass energy of 13 $\text {TeV}$ and are fully corrected for detector effects, meaning that the data can be used to constrain new-physics models beyond those shown in this paper.

Measurement of the ZZ production cross section in proton-proton collisions at √s=8 TeV using the ZZ → ℓ−ℓ+ℓ′−ℓ′+ and ZZ→ ℓ−ℓ+νν¯ decay channels with the ATLAS detector

Abstract: A measurement of the $ZZ$ production in the $\ell^{-}\ell^{+}\ell^{\prime -}\ell^{\prime +}$ and $\ell^{-}\ell^{+} u\bar{ u}$ channels $(\ell = e, \mu)$ in proton--proton collisions at $\sqrt{s} = 8$ TeV at the Large Hadron Collider at CERN, using data corresponding to an integrated luminosity of 20.3 fb$^{-1}$ collected by the ATLAS experiment in 2012 is presented. The fiducial cross sections for $ZZ\to\ell^{-}\ell^{+}\ell^{\prime -}\ell^{\prime +}$ and $ZZ\to \ell^{-}\ell^{+} u\bar{ u}$ are measured in selected phase-space regions. The total cross section for $ZZ$ events produced with both $Z$ bosons in the mass range 66 to 116 GeV is measured from the combination of the two channels to be $7.3\pm0.4\textrm{(stat)}\pm0.3\textrm{(syst)}\pm0.2\textrm{(lumi)}$ pb, which is consistent with the Standard Model prediction of $6.6^{+0.7}_{-0.6}$ pb. The differential cross sections in bins of various kinematic variables are presented. The differential event yield as a function of the transverse momentum of the leading $Z$ boson is used to set limits on anomalous neutral triple gauge boson couplings in $ZZ$ production.

Search for Diphoton Events with Large Missing Transverse Energy in 7 TeV Proton-Proton Collisions with the ATLAS Detector

Abstract: A search for diphoton events with large missing transverse energy is presented. The data were collected with the ATLAS detector in proton-proton collisions at root s = 7 TeV at the CERN Large Hadron Collider and correspond to an integrated luminosity of 3: 1 pb(-1). No excess of such events is observed above the standard model background prediction. In the context of a specific model with one universal extra dimension with compactification radius R and gravity-induced decays, values of 1/R < 729 GeV are excluded at 95% C. L., providing the most sensitive limit on this model to date.

Search for the Production of a Long-Lived Neutral Particle Decaying within the ATLAS Hadronic Calorimeter in Association with a Z Boson from pp Collisions at √s = 13 TeV

Abstract: This Letter presents a search for the production of a long-lived neutral particle (Zd) decaying within the ATLAS hadronic calorimeter, in association with a standard model (SM) Z boson produced via an intermediate scalar boson, where Z→+ (=e, μ). The data used were collected by the ATLAS detector during 2015 and 2016 pp collisions with a center-of-mass energy of s=13 TeV at the Large Hadron Collider and correspond to an integrated luminosity of 36.1±0.8 fb-1. No significant excess of events is observed above the expected background. Limits on the production cross section of the scalar boson times its decay branching fraction into the long-lived neutral particle are derived as a function of the mass of the intermediate scalar boson, the mass of the long-lived neutral particle, and its cτ from a few centimeters to one hundred meters. In the case that the intermediate scalar boson is the SM Higgs boson, its decay branching fraction to a long-lived neutral particle with a cτ approximately between 0.1 and 7 m is excluded with a 95% confidence level up to 10% for mZd between 5 and 15 GeV. © 2019 CERN for the ATLAS Collaboration. Published by the American Physical Society under the terms of the »https://creativecommons.org/licenses/by/4.0/» Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP 3 .

I and i

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 …

Machine learning

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.).

Phd by thesis

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